CN112714165B - Distributed network cooperation strategy optimization method and device based on combination mechanism - Google Patents

Distributed network cooperation strategy optimization method and device based on combination mechanism Download PDF

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CN112714165B
CN112714165B CN202011530099.8A CN202011530099A CN112714165B CN 112714165 B CN112714165 B CN 112714165B CN 202011530099 A CN202011530099 A CN 202011530099A CN 112714165 B CN112714165 B CN 112714165B
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combination
node
strategy
coefficient
preset
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CN112714165A (en
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靳丹琦
陈捷
隆弢
李文申
陈静
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Shenggeng Intelligent Technology Xi'an Research Institute Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/104—Peer-to-peer [P2P] networks
    • H04L67/1061—Peer-to-peer [P2P] networks using node-based peer discovery mechanisms
    • H04L67/1065—Discovery involving distributed pre-established resource-based relationships among peers, e.g. based on distributed hash tables [DHT] 
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The application discloses a distributed network cooperation strategy optimization method based on a combination mechanism, which comprises the steps of obtaining initial estimation results of a preset node under a plurality of candidate cooperation strategies of a distributed network at a preset moment; assigning a respective initial combining coefficient to each of the initial estimation results; according to a preset combination mechanism, the initial combination coefficient is subjected to self-adaptive adjustment to obtain an optimized combination coefficient which enables the performance of the cooperation strategy to be optimized; and obtaining an optimized estimation result of the preset node by using the optimized combination coefficient and the candidate cooperation strategy. The distributed network cooperation strategy optimization method based on the combination mechanism can overcome the defect of a single cooperation strategy, and enables the distributed network to achieve the best cooperation strategy performance in different application situations. The application also discloses a distributed network cooperation strategy optimization device based on a combination mechanism, computer equipment and a computer readable storage medium, and the distributed network cooperation strategy optimization device, the computer equipment and the computer readable storage medium have the same advantages.

Description

Distributed network cooperation strategy optimization method and device based on combination mechanism
Technical Field
The invention belongs to the technical field of self-adaptive signal processing, and particularly relates to a distributed network cooperation strategy optimization method and device based on a combination mechanism, computer equipment and a computer readable storage medium.
Background
In recent years, with the rapid development of artificial multi-agent networks and the deep understanding of human beings on natural multi-agent networks, the adaptive learning technology in the distributed network has become an important concern in the current signal processing field, and has a promoting effect on the problems of signal detection, target positioning and tracking, distributed voice enhancement, spectrum sensing, distributed image processing, biological clustering behavior simulation and the like in the multi-agent networks. In the existing adaptive signal processing field, an adaptive filter is used, the adaptive filter can adapt to or track the non-stationary random change of the external environment through self-learning of real-time stream data under the condition of no priori knowledge, and finally approaches the performance of the optimal filter under a certain criterion, and the adaptive signal processing technology is applied to the fields of communication, control, radar, sonar, earthquake, biomedicine and the like. Compared with the traditional adaptive signal processing technology, the network node not only needs to perform model estimation, signal prediction or anomaly detection according to self-observation data, but also more importantly, in a certain associated sensing area, the node needs to complete a specific global task with other connected neighbor nodes in a self-organized cooperative mode.
Specifically, the collaboration policy in the distributed network includes three types: the method comprises an increment strategy, a consistency strategy and a diffusion strategy, wherein the increment strategy requires information flow in a network to form an annular graph, namely a Hamiltonian loop, although theoretically the increment strategy requires small communication traffic and a simple communication model, establishing the Hamiltonian loop in the network is an NP difficult problem, and in addition, the loop is very sensitive to failure of nodes or links, so the increment strategy is not completely suitable for distributed online adaptive signal processing; in the consistency strategy and the diffusion strategy, each node needs to communicate with the neighbor nodes in real time, and utilizes the information exchange between the node and the neighbor nodes to cooperatively estimate the global target parameters in the network, and each node needs to acquire the information of all the neighbor nodes at each moment, so that the two strategies need more communication resources than an increment strategy, but can fully utilize the cooperation of the nodes in a distributed network structure.
However, the single cooperation strategy adopted in the prior art is limited by its design assumptions, so that the performance is better only in some situations where these assumptions are satisfied, and the performance of the best strategy cannot be achieved in different application situations.
Disclosure of Invention
In order to solve the above problems, the present invention provides a distributed network cooperation policy optimization method, apparatus, computer device and computer readable storage medium based on a combination mechanism, which can overcome the defect of a single cooperation policy, so that the distributed network can achieve the best performance of the cooperation policy in different application situations.
The invention provides a distributed network cooperation strategy optimization method based on a combined mechanism, which comprises the following steps:
at a preset moment, obtaining an initial estimation result of a preset node under a plurality of candidate cooperation strategies of the distributed network;
assigning a respective initial combining coefficient to each of the initial estimation results;
according to a preset combination mechanism, the initial combination coefficient is subjected to self-adaptive adjustment to obtain an optimized combination coefficient which enables the performance of the cooperation strategy to be optimized;
and obtaining an optimized estimation result of the preset node by using the optimized combination coefficient and the candidate cooperation strategy.
Preferably, in the method for optimizing a distributed network cooperation strategy based on a combination mechanism, the initial combination coefficient includes at least one of a convex combination coefficient and an affine combination coefficient.
Preferably, in the method for optimizing a distributed network cooperation policy based on a combination mechanism, the candidate cooperation policy includes at least one of a plurality of different types of cooperation policies with the same parameter, a plurality of same types of cooperation policies with different parameters, and a plurality of different types of cooperation policies with different parameters.
Preferably, in the method for optimizing a distributed network cooperation policy based on a combination mechanism, when the number of candidate cooperation policies is 2, the preset combination mechanism is at least one of an affine energy normalization mechanism, an affine symbol regression mechanism, a convex energy normalization mechanism and a convex symbol regression mechanism.
Preferably, in the distributed network cooperation policy optimization method based on the combination mechanism, when the number of the candidate cooperation policies is greater than 2, the preset combination mechanism is at least one of an affine multi-policy mechanism and a convex multi-policy mechanism.
Preferably, in the method for optimizing distributed network cooperation policy based on combination mechanism, when the number of candidate cooperation policies is 2 and the preset combination mechanism is an affine energy normalization mechanism, the combination coefficient is adjusted according to the affine energy normalization mechanism
Figure BDA0002851815680000031
And &>
Figure BDA0002851815680000032
Figure BDA0002851815680000033
Wherein x k,n Is the input signal of node k at time n, e k,n Is the error signal of node k at time n, i.e.
Figure BDA0002851815680000034
Figure BDA0002851815680000035
Is the step size parameter, p k,n Is calculated by the following iterative formula: />
Figure BDA0002851815680000036
The time smooth coefficient is more than 0 and less than 1, the value range of epsilon is (0, 0.5)]. And->
Figure BDA0002851815680000037
When the preset combination mechanism is an affine symbol regression mechanism, the combination coefficient is adjusted according to the affine symbol regression mechanism
Figure BDA0002851815680000038
And &>
Figure BDA0002851815680000039
Figure BDA00028518156800000310
Wherein e k,n Is the error signal at the node k at the instant n, i.e. < >>
Figure BDA00028518156800000311
Figure BDA00028518156800000312
Is the step size parameter, sgn { x } is a sign function defined as:
Figure BDA00028518156800000313
and->
Figure BDA00028518156800000314
When the preset combination mechanism is a convex energy normalization mechanism, the combination coefficient is adjusted according to the convex energy normalization mechanism
Figure BDA00028518156800000315
And &>
Figure BDA00028518156800000316
Figure BDA00028518156800000317
Wherein alpha is k,n The iterative update is performed according to: />
Figure BDA00028518156800000318
x k,n Is the input signal of node k at time n, e k,n Is the error signal at the node k at the instant n, i.e. < >>
Figure BDA00028518156800000319
Figure BDA00028518156800000320
Is the step size parameter, p k,n Is calculated by the following iterative formula: />
Figure BDA00028518156800000321
The time smooth coefficient is more than 0 and less than 1, the value range of epsilon is (0, 0.5)]At α in k,n After the update is completed, it is limited to [ - α ] + ,α + ]And is->
Figure BDA0002851815680000041
Calculated by the following formula: />
Figure BDA0002851815680000042
When the preset combination mechanism is a convex symbol regression mechanism, the combination coefficient is adjusted according to the convex symbol regression mechanism
Figure BDA0002851815680000043
And &>
Figure BDA0002851815680000044
Figure BDA0002851815680000045
Wherein alpha is k,n Updating is performed according to the following formula:
Figure BDA0002851815680000046
e k,n is the error signal at the node k at the instant n, i.e. < >>
Figure BDA0002851815680000047
Figure BDA0002851815680000048
Is the step size parameter, sgn { x } is a sign function defined as:
Figure BDA0002851815680000049
at α kn After the update is completed, it is limited to [ - α ] + ,α + ]To do so
Figure BDA00028518156800000410
Calculated by the following formula: />
Figure BDA00028518156800000411
Preferably, in the method for optimizing distributed network cooperation policy based on combination mechanism, when the number of candidate cooperation policies is greater than 2 and the preset combination mechanism is an affine multi-policy mechanism, the combination coefficient is adjusted according to the affine multi-policy mechanism
Figure BDA00028518156800000412
Figure BDA00028518156800000413
Wherein
Figure BDA00028518156800000414
Is an auxiliary parameter, and the iterative update expression is:
Figure BDA00028518156800000415
Figure BDA00028518156800000416
is the step size parameter, the estimation error e k,n Calculated by the following formula: />
Figure BDA00028518156800000417
And/or>
Figure BDA00028518156800000418
Delta value range (0, 0.5)]；
When the preset combination mechanism is a convex multi-strategy mechanism, the combination coefficient is adjusted according to the convex multi-strategy mechanism
Figure BDA00028518156800000419
Figure BDA0002851815680000051
Wherein
Figure BDA0002851815680000052
Is an auxiliary parameter, and the iterative update expression is: />
Figure BDA0002851815680000053
Figure BDA0002851815680000054
Is the step size parameter, the estimation error e k,n Calculated by the following formula: />
Figure BDA0002851815680000055
And->
Figure BDA0002851815680000056
Delta value range (0, 0.5)]In>
Figure BDA0002851815680000057
After the update is completed, it is limited to->
Figure BDA0002851815680000058
The invention provides a distributed network cooperation strategy optimization device based on a combined mechanism, which comprises:
the initial estimation result acquisition unit is used for acquiring initial estimation results of a preset node under a plurality of candidate cooperation strategies of the distributed network at a preset moment;
an initial combination coefficient allocation unit, configured to allocate a corresponding initial combination coefficient to each of the initial estimation results;
the self-adaptive adjusting unit is used for carrying out self-adaptive adjustment on the initial combination coefficient according to a preset combination mechanism to obtain an optimized combination coefficient which enables the performance of the cooperation strategy to be optimized;
and the optimized estimation result calculation unit is used for obtaining the optimized estimation result of the preset node by using the optimized combination coefficient and the candidate cooperation strategy.
The invention provides a computer device comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method for optimizing a distributed network cooperation strategy based on a combination mechanism according to any one of the above when the computer program is executed.
The invention provides a computer readable storage medium, which stores thereon a computer program, which when executed by a processor implements the steps of the method for optimizing a distributed network cooperation strategy based on a combination mechanism according to any one of the above.
As can be seen from the above description, according to the combination mechanism-based distributed network cooperation policy optimization method provided by the present invention, initial estimation results of a preset node under multiple candidate cooperation policies of a distributed network are obtained at a preset time, then a corresponding initial combination coefficient is allocated to each initial estimation result, the initial combination coefficients are adaptively adjusted according to a preset combination mechanism to obtain an optimized combination coefficient that optimizes the performance of a cooperation policy, and finally the optimized estimation results of the preset node are obtained by using the optimized combination coefficient and the candidate cooperation policies. The distributed network cooperation strategy optimization device based on the combination mechanism, the computer equipment and the computer readable storage medium provided by the invention have the same advantages as the method.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic diagram of an embodiment of a distributed network cooperation policy optimization method based on a combination mechanism according to the present invention;
FIG. 2 is a schematic diagram of a combinational architecture of a collaboration policy of a distributed network based on a combinational mechanism;
fig. 3 is a diagram of simulation results of an affine energy normalization mechanism and an affine symbol regression mechanism for M = 2;
fig. 4 is a graph of simulation results for the convex energy normalization mechanism and the convex sign regression mechanism for M = 2;
FIG. 5 is a diagram of simulation results for an affine multi-policy mechanism when M > 2;
FIG. 6 is a diagram of simulation results for M > 2 time-varying convex multi-strategy mechanism;
fig. 7 is a schematic diagram of an embodiment of a distributed network cooperation policy optimization apparatus based on a combination mechanism according to the present invention;
FIG. 8 is a diagram of an embodiment of a computer device.
Detailed Description
The core of the invention is to provide a distributed network cooperation strategy optimization method and a device based on a combined mechanism, which can overcome the defect of a single cooperation strategy, ensure that the distributed network can achieve the performance of the best cooperation strategy in different application situations, and can be applied to a diffusion strategy, an increment strategy and a consistency strategy.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In the embodiment of the present invention, the diffusion strategy is taken as an example for explanation, but the method provided by the present invention is not limited to this diffusion strategy. The signal model and the related quantities in the embodiment of the invention are as follows: considering a distributed network consisting of N nodes, at each node k an unknown parameter vector of length lx 1 needs to be estimated
Figure BDA0002851815680000071
An input vector x of length Lx 1 can be observed at node k k,n And a reference signal d k,n At time n, the data on node k is represented by the linear model->
Figure BDA0002851815680000072
Is associated with z wherein k,n Is additive noise, node k needs to utilize the input vector x k,n And a reference signal d k,n To evaluate a vector of unknown parameters>
Figure BDA0002851815680000073
Fig. 1 shows an implementation of a distributed network cooperation policy optimization method based on a combination mechanism, where fig. 1 is a schematic diagram of an embodiment of a distributed network cooperation policy optimization method based on a combination mechanism, and the method may include the following steps:
s1: at a preset moment, acquiring an initial estimation result of a preset node under a plurality of candidate cooperation strategies of a distributed network;
taking the candidate collaboration policy as a candidate diffusion policy as an example for explanation, with reference to fig. 2, fig. 2 is a group of collaboration policies of a distributed network based on a combination mechanismThe step of building a schematic diagram can be to run M candidate diffusion strategies S in parallel in the distributed network (1) 、S (2) 、……、S (M) And the node k obtains a candidate diffusion strategy S at the moment n (i) Initial estimation result of (2)
Figure BDA0002851815680000074
Where i =1,2, \8230;, M.
S2: allocating a corresponding initial combination coefficient to each initial estimation result;
specifically, M candidate diffusion strategies S on the node k may be performed at time n (1) 、S (2) 、……、S (M) Initial estimation result of (2)
Figure BDA0002851815680000075
Assigning initial combination coefficients>
Figure BDA0002851815680000076
Figure BDA0002851815680000077
S3: according to a preset combination mechanism, carrying out self-adaptive adjustment on the initial combination coefficient to obtain an optimized combination coefficient for optimizing the performance of the cooperation strategy;
the step may be specifically based on a preset combination mechanism, and the initial combination coefficient is adaptively adjusted
Figure BDA0002851815680000078
Figure BDA0002851815680000079
The cooperation strategy which leads the tracking performance to be the best corresponds to the optimized combination coefficient.
S4: and obtaining an optimized estimation result of the preset node by using the optimized combination coefficient and the candidate cooperation strategy.
The optimal combination coefficient can be used to calculate the final output result of the distributed network
Figure BDA00028518156800000710
As can be seen from the above description, in the embodiment of the distributed network cooperation strategy optimization method based on the combination mechanism provided by the present invention, since the initial estimation results of the preset node under the multiple candidate cooperation strategies of the distributed network are obtained at the preset time, then the corresponding initial combination coefficient is allocated to each initial estimation result, the initial combination coefficients are adaptively adjusted according to the preset combination mechanism to obtain the optimized combination coefficient that optimizes the performance of the cooperation strategy, and finally the optimized estimation results of the preset node are obtained by using the optimized combination coefficient and the candidate cooperation strategies, it can be seen that the multiple candidate cooperation strategies are considered here, so that the defect of a single cooperation strategy can be overcome, and the distributed network can achieve the best performance of the cooperation strategy in different application situations.
Based on the embodiment of the method for optimizing the distributed network cooperation strategy based on the combination mechanism, a specific embodiment of another method provided by the present invention further preferably selects the initial combination coefficient to include at least one of a convex combination coefficient and an affine combination coefficient, that is, only the convex combination coefficient or only the affine combination coefficient may be included, or both of them may be included.
Specifically, the affine combination coefficient may satisfy the following condition:
Figure BDA0002851815680000081
and a convex combination coefficient satisfying the following conditions: />
Figure BDA0002851815680000082
And->
Figure BDA0002851815680000083
Where i =1,2, \8230;, M.
Also, the candidate cooperation policy may preferably include at least one of a plurality of different types of cooperation policies having the same parameter, a plurality of the same type of cooperation policies having different parameters, and a plurality of different types of cooperation policies having different parameters.
The present invention provides another embodiment, which is a further optimization based on the embodiment of the distributed network cooperation policy optimization method based on the combination mechanism, that is, when the number of candidate cooperation policies is 2, the preset combination mechanism is at least one of an affine energy normalization mechanism, an affine symbol regression mechanism, a convex energy normalization mechanism, and a convex symbol regression mechanism.
The four mechanisms are described in detail below:
(1) Adjusting combining coefficients according to affine energy normalization mechanism
Figure BDA0002851815680000084
And &>
Figure BDA0002851815680000085
Figure BDA0002851815680000086
Wherein x k,n Is the input signal of node k at time n, e k,n Is the error signal at node k at time n, i.e. </R>
Figure BDA0002851815680000087
Figure BDA0002851815680000088
Is the step size parameter, p k,n Is calculated by the following iterative formula: />
Figure BDA0002851815680000089
The time smoothing coefficient is 0 < eta < 1, epsilon is a small positive number and is used for avoiding the condition that the denominator is zero, and the value range of epsilon is (0, 0.5)]The smaller the value, the smaller the deviation. And/or>
Figure BDA00028518156800000810
(2) Adjusting combining coefficients according to affine symbol regression mechanism
Figure BDA0002851815680000091
And &>
Figure BDA0002851815680000092
Figure BDA0002851815680000093
Wherein e k,n Is the error signal at the node k at the instant n, i.e. < >>
Figure BDA0002851815680000094
Figure BDA0002851815680000095
Is the step size parameter, sgn { x } is a sign function, which is defined as:
Figure BDA0002851815680000096
and->
Figure BDA0002851815680000097
(3) Adjusting the combining coefficients according to a convex energy normalization mechanism
Figure BDA0002851815680000098
And &>
Figure BDA0002851815680000099
Figure BDA00028518156800000910
Wherein alpha is k,n The iterative update is performed according to:
Figure BDA00028518156800000911
x k,n is the input signal of node k at time n, e k,n Is the error signal at node k at time n, i.e. </R>
Figure BDA00028518156800000912
Figure BDA00028518156800000913
Is the step size parameter, p k,n Calculated by the following iterative formula: />
Figure BDA00028518156800000914
The time smooth coefficient is more than 0 and less than 1, and epsilon is a small positive number used for avoiding the condition that the denominator is zero and the value range thereof is (0, 0.5)]The smaller the value, the smaller the deviation. More importantly, in alpha k,n After the update is completed, it needs to be limited to [ - α ] + ,α + ]And is->
Figure BDA00028518156800000915
Can be calculated by the following formula: />
Figure BDA00028518156800000916
(4) Adjusting the combination coefficients according to a convex symbol regression metric mechanism
Figure BDA00028518156800000917
And &>
Figure BDA00028518156800000918
Figure BDA00028518156800000919
Wherein alpha is k,n Updating is performed according to the following formula:
Figure BDA00028518156800000920
e k,n is the error signal at node k at time n, i.e. </R>
Figure BDA00028518156800000921
Figure BDA00028518156800000922
Is the step size parameter, sgn { x } is a sign function, which is defined as:
Figure BDA00028518156800000923
more importantly, in alpha k,n After the update is completed, it needs to be limited to [ - α ] + ,α + ]And is->
Figure BDA00028518156800000924
Can be calculated by the following formula: />
Figure BDA00028518156800000925
In addition, when the number of candidate cooperation strategies is greater than 2, the preset combination mechanism is at least one of an affine multi-strategy mechanism and a convex multi-strategy mechanism, that is, the affine multi-strategy mechanism and the convex multi-strategy mechanism can be adopted independently, or the affine multi-strategy mechanism and the convex multi-strategy mechanism can be adopted simultaneously. The method comprises the following specific steps:
(1) Adjusting combining coefficients according to affine multi-policy mechanism
Figure BDA0002851815680000101
Figure BDA0002851815680000102
Wherein
Figure BDA0002851815680000103
Is a newly introduced auxiliary parameter, and the iterative update expression of the auxiliary parameter is as follows:
Figure BDA0002851815680000104
/>
Figure BDA0002851815680000105
is a step size parameter, estimateError e kn Calculated by the following formula: />
Figure BDA0002851815680000106
And->
Figure BDA0002851815680000107
Delta is a small positive number to avoid the condition that the denominator is zero, and the value range of delta is (0, 0.5)]The smaller the value, the smaller the deviation.
(2) Adjusting combining coefficients according to a convex multi-strategy mechanism
Figure BDA0002851815680000108
Figure BDA0002851815680000109
Wherein
Figure BDA00028518156800001010
Is a newly introduced auxiliary parameter, and the iterative update expression of the auxiliary parameter is as follows:
Figure BDA00028518156800001011
Figure BDA00028518156800001012
is the step size parameter, the estimation error e k,n Calculated by the following formula: />
Figure BDA00028518156800001013
And->
Figure BDA00028518156800001014
Delta is a small positive number to avoid the condition that the denominator is zero, and the value range of delta is (0, 0.5)]The smaller the value, the smaller the deviation. More importantly, is to be determined in>
Figure BDA00028518156800001015
After the update is completed, it needs to be restricted to +>
Figure BDA00028518156800001016
The above embodiments are described in more detail below using three specific examples:
in a first example:
consider that
Figure BDA0002851815680000111
The non-stationary system identification application with time variation is characterized in that a distributed network is composed of N =10 nodes, and regression quantity x k,n Sum noise z k,n Are each generated by a zero mean Gaussian distribution, the weight coefficient vector->
Figure BDA0002851815680000112
Is divided into four stationary phases and three transient phases. In stationary phases, a weight coefficient vector>
Figure BDA0002851815680000113
The method is characterized in that the method is generated by standard Gaussian distribution, a weight coefficient vector in a transient stage is generated by linear interpolation of weight coefficient vectors in adjacent stationary stages, two ATC diffusion LMS strategies are considered as candidate strategies, and combined matrixes of the two ATC diffusion LMS strategies are matrixes corresponding to an identity matrix and an averaging rule respectively. For the affine energy normalization mechanism, set ε =0.05, η =0.95 and £>
Figure BDA0002851815680000114
Setting ≦ for affine sign regression mechanism>
Figure BDA0002851815680000115
The result is shown in fig. 3, fig. 3 is a graph of a simulation result of an affine energy normalization mechanism and an affine symbol regression mechanism for M =2, it can be seen from fig. 3 (a) that the best candidate policy performance can be achieved at different stages, and the change of the combination coefficient of the node 4 and the node 9 in fig. 3 (b) further verifies the effectiveness of the distributed network diffusion policy based on the combination mechanism.
The second example:
consider that
Figure BDA0002851815680000116
The non-stationary system identification application with time variation is characterized in that a distributed network is composed of N =10 nodes, and regression quantity x k,n Sum noise z k,n Are each generated by a zero-mean gaussian distribution, the weight coefficient vector->
Figure BDA0002851815680000117
The variation of (c) is divided into five stationary phases and four transient phases. In stationary phases, a weight coefficient vector>
Figure BDA0002851815680000118
The method is characterized in that the method is generated by standard Gaussian distribution, a weight coefficient vector in a transient stage is generated by linear interpolation of weight coefficient vectors in adjacent stationary stages, two ATC diffusion LMS strategies are considered as candidate strategies, and combined matrixes of the two ATC diffusion LMS strategies are matrixes corresponding to an identity matrix and an averaging rule respectively. For the convex energy normalization mechanism, ε =0.05, η =0.95 and £ are set>
Figure BDA0002851815680000119
Setting ^ for the convex symbol regression mechanism>
Figure BDA00028518156800001110
The result is shown in fig. 4, fig. 4 is a graph of a simulation result of a convex energy normalization mechanism and a convex symbol regression mechanism for M =2, it can be seen from fig. 4 (a) that the best candidate policy performance can be achieved at different stages, and the validity of the distributed network diffusion policy based on the combination mechanism is further verified by the change of the combination coefficient of the node 4 and the node 9 in fig. 4 (b).
In a third example:
consider that
Figure BDA00028518156800001111
The non-stationary system identification application with time variation is characterized in that a distributed network is composed of N =10 nodes, and regression quantity x k,n And noise z k,n Are each generated by a zero mean Gaussian distribution, the weight coefficient vector->
Figure BDA00028518156800001112
Is divided into four stationary phases and three transient phases. In the stationary phase, the weight coefficient vector->
Figure BDA00028518156800001113
The method is generated by standard Gaussian distribution, the weight coefficient vector of a transient stage is generated by linear interpolation of the weight coefficient vectors of adjacent stationary stages, three ATC diffusion LMS strategies are considered as candidate strategies, and a candidate strategy S (1) The combined matrix of (2) is an identity matrix and has smaller network step length; candidate policy S (2) The combined matrix of (2) is a matrix corresponding to the average rule; candidate policy S (3) The combined matrix is an identity matrix and has a larger network step size, an affine multi-policy mechanism is considered, the result is shown in fig. 5, fig. 5 is a simulation result diagram of the affine multi-policy mechanism when M > 2, it can be seen from fig. 5 (a) that the best candidate policy performance can be achieved at different stages, and the validity of the distributed network diffusion policy based on the combined mechanism is further verified by the change of the combined coefficient of the node 9 in fig. 5 (b).
The fourth example:
consider that
Figure BDA0002851815680000121
The non-stationary system identification application with time variation is characterized in that a distributed network is composed of N =10 nodes, and regression quantity x k,n Sum noise z k,n Are each generated by a zero mean Gaussian distribution, the weight coefficient vector->
Figure BDA0002851815680000122
The variation of (c) is divided into five stationary phases and four transient phases. In stationary phases, a weight coefficient vector>
Figure BDA0002851815680000123
The method is generated by standard Gaussian distribution, the weight coefficient vector of a transient stage is generated by linear interpolation of the weight coefficient vectors of adjacent stationary stages, three ATC diffusion LMS strategies are considered as candidate strategies, and a candidate strategy S (1) The combined matrix of (2) is an identity matrix and has a smaller network step length; candidate policy S (2) The combined matrix of (a) is a matrix corresponding to the average rule; candidate policy S (3) The combined matrix of (2) is an identity matrix, and has a larger network step length, and a convex multi-strategy mechanism is considered.
The result is shown in fig. 6, fig. 6 is a simulation result diagram of the M > 2 time-varying multi-policy mechanism, and it can be seen from fig. 6 (a) that the best candidate policy performance can be achieved at different stages, and the change of the node 9 combination coefficient in fig. 6 (b) further verifies the effectiveness of the distributed network flooding policy based on the combination mechanism.
In summary, the method provided by the present invention adopts multiple mechanisms to adaptively adjust the combination coefficients, and has the advantages that the combination mechanism is utilized to overcome the defect of a single diffusion strategy, and different candidate strategies are selected to enable the combination strategy to achieve the best performance in different application situations.
Fig. 7 shows an embodiment of a distributed network cooperation policy optimization apparatus based on a combination mechanism provided by the present invention, and fig. 7 is a schematic diagram of an embodiment of a distributed network cooperation policy optimization apparatus based on a combination mechanism provided by the present invention, the apparatus including:
an initial estimation result obtaining unit 701, configured to obtain, at a preset time, initial estimation results of a preset node under multiple candidate cooperation policies of a distributed network, where specifically, M candidate diffusion policies S may be run in parallel in the distributed network (1) 、S (2) 、……、S (M) And the node k obtains a candidate diffusion strategy S at the moment n (i) Initial estimation result of
Figure BDA0002851815680000124
Wherein i =1,2, \8230;, M;
initial combinationA coefficient assigning unit 702, configured to assign a corresponding initial combination coefficient to each initial estimation result, specifically, M candidate diffusion strategies S on the node k at time n (1) 、S (2) 、……、S (M) Initial estimation result of (2)
Figure BDA0002851815680000131
Respectively assign an initial combination coefficient->
Figure BDA0002851815680000132
The adaptive adjusting unit 703 is configured to perform adaptive adjustment on the initial combination coefficient according to a preset combination mechanism to obtain an optimized combination coefficient that optimizes the performance of the cooperation strategy, and specifically, the adaptive adjusting unit may adaptively adjust the initial combination coefficient based on the preset combination mechanism
Figure BDA0002851815680000133
The cooperation strategy with the best tracking performance corresponds to the optimized combination coefficient;
an optimized estimation result calculating unit 704, configured to obtain an optimized estimation result of the preset node by using the optimized combination coefficient and the candidate cooperation policy, and specifically, the optimized combination coefficient may be used to calculate a final output result of the distributed network
Figure BDA0002851815680000134
Another specific embodiment of the apparatus provided by the present invention is, on the basis of the above embodiment of the apparatus for optimizing a distributed network cooperation policy based on a combination mechanism, further specifically, using the above initial combination coefficient allocating unit to allocate a corresponding convex combination coefficient and/or affine combination coefficient to each initial estimation result. That is, only the convex combination coefficient may be used, only the affine combination coefficient may be used, or both of them may be included. Specifically, the affine combination coefficient may satisfy the following condition:
Figure BDA0002851815680000135
and a convex combination coefficient satisfying the following condition: />
Figure BDA0002851815680000136
And->
Figure BDA0002851815680000137
Where i =1,2, \8230;, M.
Moreover, the initial estimation result obtaining unit is specifically configured to obtain, at a preset time, an initial estimation result of a preset node under multiple different types of cooperation policies with the same parameter and/or multiple same types of cooperation policies with different parameters and/or multiple different types of cooperation policies with different parameters.
A further specific embodiment of the apparatus provided by the present invention is based on the embodiment of the distributed network cooperation policy optimization apparatus based on the combination mechanism, and the adaptive adjustment unit may be specifically configured to, when the number of candidate cooperation policies is 2, adaptively adjust the initial combination coefficient according to an affine energy normalization mechanism and/or an affine symbol regression metric mechanism and/or a convex energy normalization mechanism and/or a convex symbol regression metric mechanism, to obtain an optimized combination coefficient that optimizes the cooperation policy performance. That is, at least one of the four mechanisms may be selected, and the specific four mechanisms are described above and will not be described herein again.
In addition, the adaptive adjustment unit may be further specifically configured to, when the number of the candidate cooperation strategies is greater than 2, perform adaptive adjustment on the initial combination coefficient according to an affine multi-strategy mechanism and/or a convex multi-strategy mechanism to obtain an optimized combination coefficient that optimizes performance of the cooperation strategies. That is, the affine multi-policy mechanism, the convex multi-policy mechanism, or both of them may be separately adopted, and these two mechanisms are also described in detail above, and therefore are not described herein again.
Fig. 8 shows an embodiment of a computer device provided by the present invention, where fig. 8 is a schematic diagram of an embodiment of a computer device provided by the present invention, and the computer device includes:
a memory 801 for storing a computer program;
a processor 802, configured to execute the computer program to implement the steps of the method for optimizing a distributed network cooperation policy based on a combination mechanism according to any of the above embodiments.
In an embodiment of a computer-readable storage medium, a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements the steps of the method for optimizing a distributed network cooperation policy based on a combination mechanism as provided in any one of the above embodiments.
The embodiments of the foregoing distributed network cooperation policy optimization apparatus, computer device, and computer-readable storage medium based on a combination mechanism provided by the present invention can overcome the defect of a single cooperation policy, so that the distributed network can achieve the performance of the best cooperation policy in different application situations, and can be applied to, but not limited to, a diffusion policy, an incremental policy, and a consistency policy.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (7)

1. A distributed network cooperation strategy optimization method based on a combination mechanism is characterized by comprising the following steps:
at a preset moment, obtaining an initial estimation result of a preset node under a plurality of candidate cooperation strategies of the distributed network;
assigning a respective initial combining coefficient to each of the initial estimation results;
according to a preset combination mechanism, the initial combination coefficient is subjected to self-adaptive adjustment to obtain an optimized combination coefficient which enables the performance of the cooperation strategy to be optimized;
obtaining an optimized estimation result of the preset node by using the optimized combination coefficient and the candidate cooperation strategy;
when the number of the candidate cooperation strategies is 2, the preset combination mechanism is at least one of an affine energy normalization mechanism, an affine symbol regression mechanism, a convex energy normalization mechanism and a convex symbol regression mechanism;
when the number of the candidate cooperation strategies is 2 and the preset combination mechanism is an affine energy normalization mechanism, adjusting a combination coefficient according to the affine energy normalization mechanism
Figure FDA0004063374320000011
And &>
Figure FDA0004063374320000012
Figure FDA0004063374320000013
Wherein x k,n Is the input signal of node k at time n, e k,n Is the error signal at the node k at the instant n, i.e. < >>
Figure FDA0004063374320000014
d k,n Is the reference signal of node k at time n->
Figure FDA0004063374320000015
Is the step size parameter, p k,n Calculated by the following iterative formula: />
Figure FDA0004063374320000016
The time smooth coefficient is more than 0 and less than 1, the value range of epsilon is (0, 0.5)]The positive number of (2) is used for avoiding the condition that the denominator is zero, and the smaller the value is, the smaller the deviation is; while
Figure FDA0004063374320000017
Distribution ofFinal output result in a network>
Figure FDA0004063374320000018
When the preset combination mechanism is an affine symbol regression mechanism, the combination coefficient is adjusted according to the affine symbol regression mechanism
Figure FDA0004063374320000019
And &>
Figure FDA00040633743200000110
Figure FDA00040633743200000111
Wherein e k,n Is the error signal of node k at time n, i.e.
Figure FDA00040633743200000112
Figure FDA00040633743200000113
Is the step size parameter, sgn { x } is a sign function defined as: />
Figure FDA00040633743200000114
And then
Figure FDA00040633743200000115
When the preset combination mechanism is a convex energy normalization mechanism, the combination coefficient is adjusted according to the convex energy normalization mechanism
Figure FDA00040633743200000116
And &>
Figure FDA00040633743200000117
Figure FDA00040633743200000118
Wherein alpha is k,n The iterative update is performed according to:
Figure FDA00040633743200000119
x k,n is the input signal of node k at time n, e k,n Is the error signal at the node k at the instant n, i.e. < >>
Figure FDA0004063374320000021
Figure FDA0004063374320000022
Is the step size parameter, p k，n Calculated by the following iterative formula: />
Figure FDA0004063374320000023
The time smooth coefficient is more than 0 and less than 1, the value range of epsilon is (0, 0.5)]At α k,n After the update is completed, it is limited to [ - α ] + ,α + ]And is->
Figure FDA0004063374320000024
Calculated by the following formula: />
Figure FDA0004063374320000025
/>
When the preset combination mechanism is a convex symbol regression mechanism, the combination coefficient is adjusted according to the convex symbol regression mechanism
Figure FDA0004063374320000026
And &>
Figure FDA0004063374320000027
Figure FDA0004063374320000028
Wherein alpha is k,n Updating is performed according to the following formula:
Figure FDA0004063374320000029
e k,n is the error signal at the node k at the instant n, i.e. < >>
Figure FDA00040633743200000210
Figure FDA00040633743200000211
Is the step size parameter, sgn { x } is a sign function defined as:
Figure FDA00040633743200000212
at α k,n After the update is completed, it is limited to [ - α ] + ,α + ]To do so
Figure FDA00040633743200000213
Calculated by the following formula: />
Figure FDA00040633743200000214
When the number of the candidate cooperation strategies is more than 2 and the preset combination mechanism is an affine multi-strategy mechanism, adjusting the combination coefficient according to the affine multi-strategy mechanism
Figure FDA00040633743200000215
Figure FDA00040633743200000216
Wherein
Figure FDA00040633743200000217
Is assisted byParameters, the iterative update expression of which is:
Figure FDA00040633743200000218
Figure FDA00040633743200000219
is the step size parameter, the estimation error e k,n Calculated by the following formula: />
Figure FDA00040633743200000220
And->
Figure FDA00040633743200000221
Delta value range (0, 0.5)](ii) a Running M candidate diffusion strategies S in parallel in a distributed network (1) 、S (2) 、……、S (M) The node k obtains a candidate diffusion strategy S at the moment n (i) Is evaluated->
Figure FDA00040633743200000222
Wherein i =1,2, \8230;, M;
when the preset combination mechanism is a convex multi-strategy mechanism, the combination coefficient is adjusted according to the convex multi-strategy mechanism
Figure FDA00040633743200000223
Figure FDA00040633743200000224
Wherein
Figure FDA0004063374320000031
Is an auxiliary parameter, and the iterative update expression is:
Figure FDA0004063374320000032
Figure FDA0004063374320000033
is the step size parameter, the estimation error e k,n Calculated by the following formula: />
Figure FDA0004063374320000034
And/or>
Figure FDA0004063374320000035
δ is a small positive number->
Figure FDA0004063374320000036
After the update is completed, it is limited to->
Figure FDA0004063374320000037
/>
2. The combination-mechanism-based distributed network cooperation strategy optimization method according to claim 1, wherein the initial combination coefficient comprises at least one of a convex combination coefficient and an affine combination coefficient.
3. The method of claim 1, wherein the candidate collaboration policies comprise at least one of a plurality of different types of collaboration policies with the same parameters, a plurality of the same type of collaboration policies with different parameters, and a plurality of different types of collaboration policies with different parameters.
4. The method according to claim 1, wherein when the number of candidate cooperation policies is greater than 2, the predetermined combination mechanism is at least one of an affine multi-policy mechanism and a convex multi-policy mechanism.
5. A distributed network cooperation strategy optimization device based on a combination mechanism is characterized by comprising the following components:
the initial estimation result acquisition unit is used for acquiring initial estimation results of a preset node under a plurality of candidate cooperation strategies of the distributed network at a preset moment;
an initial combination coefficient allocation unit, configured to allocate a corresponding initial combination coefficient to each of the initial estimation results;
the self-adaptive adjusting unit is used for carrying out self-adaptive adjustment on the initial combination coefficient according to a preset combination mechanism to obtain an optimized combination coefficient which enables the performance of the cooperation strategy to be optimized;
the optimized estimation result calculation unit is used for obtaining the optimized estimation result of the preset node by using the optimized combination coefficient and the candidate cooperation strategy;
when the number of the candidate cooperation strategies is 2, the preset combination mechanism is at least one of an affine energy normalization mechanism, an affine symbol regression mechanism, a convex energy normalization mechanism and a convex symbol regression mechanism;
when the number of the candidate cooperation strategies is 2 and the preset combination mechanism is an affine energy normalization mechanism, adjusting the combination coefficient according to the affine energy normalization mechanism
Figure FDA0004063374320000038
And &>
Figure FDA0004063374320000039
Figure FDA00040633743200000310
Wherein x k,n Is the input signal of node k at time n, e k,n Is the error signal at the node k at the instant n, i.e. < >>
Figure FDA00040633743200000311
d k,n Is the reference signal of node k at time n->
Figure FDA0004063374320000041
Is the step size parameter, p k,n Is calculated by the following iterative formula: />
Figure FDA0004063374320000042
The time smooth coefficient is more than 0 and less than 1, the value range of epsilon is (0, 0.5)]The positive number of (2) is used for avoiding the condition that the denominator is zero, and the smaller the value is, the smaller the deviation is; while
Figure FDA0004063374320000043
Final output result->
Figure FDA0004063374320000044
When the preset combination mechanism is an affine symbol regression mechanism, the combination coefficient is adjusted according to the affine symbol regression mechanism
Figure FDA0004063374320000045
And &>
Figure FDA0004063374320000046
Figure FDA0004063374320000047
Wherein e k,n Is the error signal of node k at time n, i.e.
Figure FDA0004063374320000048
Figure FDA0004063374320000049
Is the step size parameter, sgn { x } is a sign function defined as:
Figure FDA00040633743200000410
and->
Figure FDA00040633743200000411
When the preset combination mechanism is a convex energy normalization mechanism, the combination coefficient is adjusted according to the convex energy normalization mechanism
Figure FDA00040633743200000412
And &>
Figure FDA00040633743200000413
Figure FDA00040633743200000414
Wherein alpha is k,n The iterative update is performed according to:
Figure FDA00040633743200000415
x k,n is the input signal of node k at time n, e k,n Is the error signal at the node k at the instant n, i.e. < >>
Figure FDA00040633743200000416
Figure FDA00040633743200000417
Is the step size parameter, p k,n Is calculated by the following iterative formula: />
Figure FDA00040633743200000418
The time smooth coefficient is more than 0 and less than 1, the value range of epsilon is (0, 0.5)]At α k,n After the update is completed, it is limited to [ - α ] + ,α + ]And is->
Figure FDA00040633743200000419
Calculated by the following formula: />
Figure FDA00040633743200000420
When the preset combination mechanism is a convex symbol regression mechanism, the combination coefficient is adjusted according to the convex symbol regression mechanism
Figure FDA00040633743200000421
And &>
Figure FDA00040633743200000422
Figure FDA00040633743200000423
Wherein alpha is k,n The update is made according to the following equation:
Figure FDA00040633743200000424
e k,n is the error signal at node k at time n, i.e. </R>
Figure FDA00040633743200000425
Figure FDA00040633743200000426
Is the step size parameter, sgn { x } is a sign function defined as:
Figure FDA00040633743200000427
at α k,n After the update is completed, it is limited to [ - α ] + ,α + ]To is that
Figure FDA00040633743200000428
Calculated by the following formula: />
Figure FDA00040633743200000429
When the number of the candidate cooperation strategies is more than 2 and the preset combination mechanism is an affine multi-strategy mechanism, adjusting a combination coefficient according to the affine multi-strategy mechanism
Figure FDA00040633743200000430
Figure FDA0004063374320000051
Wherein
Figure FDA0004063374320000052
Is an auxiliary parameter, and the iterative update expression is:
Figure FDA0004063374320000053
Figure FDA0004063374320000054
is the step size parameter, the estimation error e k,n Calculated by the following formula: />
Figure FDA0004063374320000055
And->
Figure FDA0004063374320000056
Delta value range (0, 0.5)](ii) a Running M candidate diffusion strategies S in parallel in a distributed network (1) 、S (2) 、……、S (M) The node k obtains a candidate diffusion strategy S at the moment n (i) Is evaluated->
Figure FDA0004063374320000057
Wherein i =1,2, \8230;, M;
when the preset combination mechanism is a convex multi-strategy mechanism, the rootAdjusting combination coefficients according to convex multi-strategy mechanism
Figure FDA0004063374320000058
/>
Figure FDA0004063374320000059
Figure FDA00040633743200000510
Figure FDA00040633743200000511
Is the step size parameter, the estimation error e k,n Calculated by the following formula: />
Figure FDA00040633743200000512
And->
Figure FDA00040633743200000513
δ is a small positive number->
Figure FDA00040633743200000514
After the update is completed, it is restricted to +>
Figure FDA00040633743200000515
6. A computer device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the method for optimizing a distributed network cooperation strategy based on a combined mechanism according to any one of claims 1 to 4 when executing the computer program.
7. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program, which when executed by a processor implements the steps of the method for distributed network cooperation policy optimization based on combinational mechanism according to any one of claims 1 to 4.
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