CN112714165A - 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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CN112714165A
CN112714165A CN202011530099.8A CN202011530099A CN112714165A CN 112714165 A CN112714165 A CN 112714165A CN 202011530099 A CN202011530099 A CN 202011530099A CN 112714165 A CN112714165 A CN 112714165A
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combination
preset
coefficient
cooperation
distributed network
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CN112714165B (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
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    • 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, a computer device and a computer readable storage medium, which 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 method, an apparatus, a computer device and a computer readable storage medium for optimizing a distributed network cooperation policy based on a combination mechanism, which can overcome the defect of a single cooperation policy, and enable the distributed network to 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, acquiring an initial estimation result of a preset node under a plurality of candidate cooperation strategies of a 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 method for optimizing distributed network cooperation policies based on a combination mechanism, when the number of 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 xk,nIs the input signal of node k at time n, ek,nIs the error signal of node k at time n, i.e.
Figure BDA0002851815680000034
Figure BDA0002851815680000035
Is the step size parameter, pk,nIs calculated by the following iterative formula:
Figure BDA0002851815680000036
the time smooth coefficient is more than 0 and less than 1, and the value range of epsilon is (0, 0.5)]. While
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 ek,nIs the error signal of node k at time n, i.e.
Figure BDA00028518156800000311
Figure BDA00028518156800000312
Is the step size parameter, sgn { x } is a sign function defined as:
Figure BDA00028518156800000313
while
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 isk,nThe iterative update is performed according to:
Figure BDA00028518156800000318
xk,nis the input signal of node k at time n, ek,nIs the error signal of node k at time n, i.e.
Figure BDA00028518156800000319
Figure BDA00028518156800000320
Is the step size parameter, pk,nIs calculated by the following iterative formula:
Figure BDA00028518156800000321
the time smooth coefficient is more than 0 and less than 1, and the value range of epsilon is (0, 0.5)]At αk,nAfter the update is completed, it is limited to [ - α ]+,α+]To do so
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 isk,nUpdating is performed according to the following formula:
Figure BDA0002851815680000046
ek,nis the error signal of node k at time n, i.e.
Figure BDA0002851815680000047
Figure BDA0002851815680000048
Is the step size parameter, sgn { x } is a sign function defined as:
Figure BDA0002851815680000049
at αknAfter 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 ek,nCalculated by the following formula:
Figure BDA00028518156800000417
while
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 ek,nCalculated by the following formula:
Figure BDA0002851815680000055
while
Figure BDA0002851815680000056
Delta value range (0, 0.5)]In a
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 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 when M is 2;
fig. 4 is a diagram of simulation results of the convex energy normalization mechanism and the convex symbol regression mechanism when M is 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 the convex multi-strategy mechanism when M > 2;
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 schematic diagram of an embodiment of a computer device provided in the present invention.
Detailed Description
The core of the invention is to provide a distributed network cooperation strategy optimization method and device based on a combination mechanism, which can overcome the defect of a single cooperation strategy, enable the distributed network to achieve the best performance of the 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within 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 quantity 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 kk,nAnd a reference signal dk,nAt time n, the data at node k is modeled by a linear model
Figure BDA0002851815680000072
Is related, wherein zk,nIs additive noise, node k needs to utilize the input vector xk,nAnd a reference signal dk,nTo estimate the unknown parameter vector
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 cooperation policy as the candidate diffusion policy as an example for explanation, with reference to fig. 2, fig. 2 is a schematic diagram of a combination architecture of cooperation policies of a distributed network based on a combination mechanism, and the step may be to run M candidate diffusion policies S in parallel in the distributed network(1)、S(2)、……、S(M)The node k obtains a candidate diffusion strategy S at the moment n(i)Initial estimation result of
Figure BDA0002851815680000074
Where i is 1,2, …, M.
S2: allocating a corresponding initial combination coefficient to each initial estimation result;
specifically, M candidate diffusion strategies S on node k may be performed at time n(1)、S(2)、……、S(M)Initial estimation result of
Figure BDA0002851815680000075
Assigning initial combining coefficients, respectively
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 can be specifically based on a preset combination mechanism, and the initial combination coefficient is adjusted adaptively
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 policy optimization method based on the combination mechanism, since the initial estimation results of the preset node under the multiple candidate cooperation policies 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 policy, and finally the optimized estimation results of the preset node are obtained by using the optimized combination coefficient and the candidate cooperation policies, it can be seen that the multiple candidate cooperation policies are considered here, so that the defect of a single cooperation policy can be overcome, and the distributed network can achieve the best performance of the cooperation policy 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 following may be satisfiedAffine combination coefficients of the following conditions:
Figure BDA0002851815680000081
and a convex combination coefficient satisfying the following conditions:
Figure BDA0002851815680000082
and is
Figure BDA0002851815680000083
Where i is 1,2, …, 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 xk,nIs the input signal of node k at time n, ek,nIs the error signal of node k at time n, i.e.
Figure BDA0002851815680000087
Figure BDA0002851815680000088
Is the step size parameter, pk,nIs 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. While
Figure BDA00028518156800000810
(2) Adjusting combining coefficients according to affine symbol regression mechanism
Figure BDA0002851815680000091
And
Figure BDA0002851815680000092
Figure BDA0002851815680000093
wherein ek,nIs the error signal of node k at time n, i.e.
Figure BDA0002851815680000094
Figure BDA0002851815680000095
Is the step size parameter, sgn { x } is a sign function, which is defined as:
Figure BDA0002851815680000096
while
Figure BDA0002851815680000097
(3) Adjusting the combining coefficients according to a convex energy normalization mechanism
Figure BDA0002851815680000098
And
Figure BDA0002851815680000099
Figure BDA00028518156800000910
wherein alpha isk,nThe iterative update is performed according to:
Figure BDA00028518156800000911
xk,nis the input signal of node k at time n, ek,nIs the error signal of node k at time n, i.e.
Figure BDA00028518156800000912
Figure BDA00028518156800000913
Is the step size parameter, pk,nIs calculated by the following iterative formula:
Figure BDA00028518156800000914
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. More importantly, in alphak,nAfter the update is completed, it needs to be limited to [ - α ]+,α+]To do so
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 isk,nUpdating is performed according to the following formula:
Figure BDA00028518156800000920
ek,nis the error signal of node k at time n, i.e.
Figure BDA00028518156800000921
Figure BDA00028518156800000922
Is the step size parameter, sgn { x } is a sign function, which is defined as:
Figure BDA00028518156800000923
more importantly, in alphak,nAfter the update is completed, it needs to be limited to [ - α ]+,α+]To do so
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 the step size parameter, the estimation error eknCalculated by the following formula:
Figure BDA0002851815680000106
while
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 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 ek,nCalculated by the following formula:
Figure BDA00028518156800001013
while
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, in
Figure BDA00028518156800001015
After the update is completed, it needs to be limited to
Figure BDA00028518156800001016
The above embodiments are described in more detail below using three specific examples:
the first example is as follows:
consider that
Figure BDA0002851815680000111
The non-stationary system identification application with time variation, the distributed network is composed of N-10 nodes, and the regression quantity xk,nAnd noise zk,nAll generated by a zero mean Gaussian distribution, weight coefficient vector
Figure BDA0002851815680000112
The variation of (c) is divided into four stationary phases and three transient phases. In the stationary phase, the 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
for affine symbolic regression mechanism, setting
Figure BDA0002851815680000115
The result is shown in fig. 3, fig. 3 is a graph of the simulation result of the affine energy normalization mechanism and the affine symbol regression mechanism when M is 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, the distributed network is composed of N-10 nodes, and the regression quantity xk,nAnd noise zk,nAll generated by a zero mean Gaussian distribution, weight coefficient vector
Figure BDA0002851815680000117
The variation of (c) is divided into five stationary phases and four transient phases. In the stationary phase, the 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, set ε 0.05, η 0.95 and
Figure BDA0002851815680000119
for convex symbol regression mechanism, setting
Figure BDA00028518156800001110
The result is shown in fig. 4, fig. 4 is a graph of a simulation result of the convex energy normalization mechanism and the convex symbol regression mechanism when M is 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).
The third example:
consider that
Figure BDA00028518156800001111
The non-stationary system identification application with time variation, the distributed network is composed of N-10 nodes, and the regression quantity xk,nAnd noise zk,nAll generated by a zero mean Gaussian distribution, weight coefficient vector
Figure BDA00028518156800001112
The variation of (c) 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 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 (a) is an identity matrix and has a larger network step size, and considering the affine multi-policy mechanism, the result is as shown in fig. 5, fig. 5 is a simulation result diagram of the affine multi-policy mechanism when M > 2, and it can be seen from fig. 5(a) that the best candidate policy performance can be achieved at different stages, and the change of the combination coefficient of the node 9 in fig. 5(b) further verifies the effectiveness of the distributed network diffusion policy based on the combined mechanism.
The fourth example:
consider that
Figure BDA0002851815680000121
The non-stationary system identification application with time variation, the distributed network is composed of N-10 nodes, and the regression quantity xk,nAnd noise zk,nAll generated by a zero mean Gaussian distribution, weight coefficient vector
Figure BDA0002851815680000122
The variation of (c) is divided into five stationary phases and four transient phases. In the stationary phase, the 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, which 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 performance of the best strategy in different application situations.
Fig. 7 shows an embodiment of a distributed network cooperation policy optimization apparatus based on a combination mechanism, where fig. 7 is a schematic diagram of an embodiment of a distributed network cooperation policy optimization apparatus based on a combination mechanism, where the apparatus includes:
initial estimation result obtainingAn obtaining unit 701, configured to obtain, at a preset time, an initial estimation result of a preset node under multiple candidate cooperation policies of a distributed network, specifically, M candidate diffusion policies S may be run in parallel in the distributed network(1)、S(2)、……、S(M)The node k obtains a candidate diffusion strategy S at the moment n(i)Initial estimation result of
Figure BDA0002851815680000124
Wherein i is 1,2, …, M;
an initial combination 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
Figure BDA0002851815680000131
Assigning initial combining coefficients, respectively
Figure BDA0002851815680000132
The adaptive adjustment 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, may adaptively adjust the initial combination coefficient based on the preset combination mechanism
Figure BDA0002851815680000133
The cooperation strategy which leads the tracking performance to be the best 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, may calculate a final output result of the distributed network by using the optimized combination coefficient
Figure BDA0002851815680000134
Another embodiment of the present invention is the device as described aboveOn the basis of the embodiment of the distributed network cooperation strategy optimization device based on the combination mechanism, the initial combination coefficient allocation unit is further specifically used for allocating 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 is
Figure BDA0002851815680000137
Where i is 1,2, …, 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 can be selected, and the specific four mechanisms are as 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 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, so as to obtain an optimized combination coefficient that optimizes the 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 the 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 (10)

1. A distributed network cooperation strategy optimization method based on a combination mechanism is characterized by comprising the following steps:
at a preset moment, acquiring an initial estimation result of a preset node under a plurality of candidate cooperation strategies of a 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.
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 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.
5. The method according to claim 1, wherein when the number of candidate collaboration 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.
6. The method as claimed in claim 4, wherein when the number of the candidate cooperation strategies 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 FDA0002851815670000011
And
Figure FDA0002851815670000012
Figure FDA0002851815670000013
wherein xk,nIs the input signal of node k at time n, ek,nIs the error signal of node k at time n, i.e.
Figure FDA0002851815670000021
Figure FDA0002851815670000022
Is the step size parameter, pk,nIs calculated by the following iterative formula:
Figure FDA0002851815670000023
the time smooth coefficient is more than 0 and less than 1, and the value range of epsilon is (0, 0.5)]. While
Figure FDA0002851815670000024
When the preset combination mechanism is an affine symbol regression mechanism, the combination coefficient is adjusted according to the affine symbol regression mechanism
Figure FDA0002851815670000025
And
Figure FDA0002851815670000026
Figure FDA0002851815670000027
wherein ek,nIs the error signal of node k at time n, i.e.
Figure FDA0002851815670000028
Figure FDA0002851815670000029
Is the step size parameter, sgn { x } is a sign function defined as:
Figure FDA00028518156700000210
while
Figure FDA00028518156700000211
When the preset combination mechanism is a convex energy normalization mechanism, the combination coefficient is adjusted according to the convex energy normalization mechanism
Figure FDA00028518156700000212
And
Figure FDA00028518156700000213
Figure FDA00028518156700000214
wherein alpha isk,nThe iterative update is performed according to:
Figure FDA00028518156700000215
xk,nis the input signal of node k at time n, ek,nIs node kError signals at time n, i.e.
Figure FDA00028518156700000216
Figure FDA00028518156700000217
Is the step size parameter, pk,nIs calculated by the following iterative formula:
Figure FDA00028518156700000218
the time smooth coefficient is more than 0 and less than 1, and the value range of epsilon is (0, 0.5)]At αk,nAfter the update is completed, it is limited to [ - α ]+,α+]To do so
Figure FDA00028518156700000219
Calculated by the following formula:
Figure FDA00028518156700000220
when the preset combination mechanism is a convex symbol regression mechanism, the combination coefficient is adjusted according to the convex symbol regression mechanism
Figure FDA00028518156700000221
And
Figure FDA00028518156700000222
Figure FDA00028518156700000223
wherein alpha isk,nUpdating is performed according to the following formula:
Figure FDA00028518156700000224
ek,nis the error signal of node k at time n, i.e.
Figure FDA00028518156700000225
Figure FDA00028518156700000226
Is the step size parameter, sgn { x } is a sign function defined as:
Figure FDA0002851815670000031
at αk,nAfter the update is completed, it is limited to [ - α ]+,α+]To do so
Figure FDA0002851815670000032
Calculated by the following formula:
Figure FDA0002851815670000033
7. the method as claimed in claim 5, wherein when the number of candidate cooperation strategies is greater than 2 and the preset combination mechanism is an affine multi-strategy mechanism, the combination coefficients are adjusted according to the affine multi-strategy mechanism
Figure FDA0002851815670000034
Figure FDA0002851815670000035
Wherein
Figure FDA0002851815670000036
Is an auxiliary parameter, and the iterative update expression is:
Figure FDA0002851815670000037
Figure FDA0002851815670000038
is the step size parameter, the estimation error ek,nCalculated by the following formula:
Figure FDA0002851815670000039
while
Figure FDA00028518156700000310
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 FDA00028518156700000311
Figure FDA00028518156700000312
Wherein
Figure FDA00028518156700000313
Is an auxiliary parameter, and the iterative update expression is:
Figure FDA00028518156700000314
Figure FDA00028518156700000315
is the step size parameter, the estimation error ek,nCalculated by the following formula:
Figure FDA00028518156700000316
while
Figure FDA0002851815670000041
δ is a small positive number, in
Figure FDA0002851815670000042
After the update is completed, it is limited to
Figure FDA0002851815670000043
8. 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;
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.
9. 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 7 when executing the computer program.
10. 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 7.
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