CN102592171A - Method and device for predicting cognitive network performance based on BP (Back Propagation) neural network - Google Patents
Method and device for predicting cognitive network performance based on BP (Back Propagation) neural network Download PDFInfo
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Abstract
The invention discloses a method for predicting the cognitive network performance based on a BP (Back Propagation) neural network. According to the method, the predication of the cognitive network performance is carried out by using the BP neural network and taking a protocol stack parameter and a performance parameter as inputs. The invention also discloses a device for predicting the cognitive network performance based on the BP neural network. The device comprises an information perception module, a data preprocessing module and a prediction module which are sequentially connected according to the signal flow direction. The cognitive network performance at future time can be accurately predicted, the cognitive network can be self-adaptively reacted in a network environment and the controllability, the management and the reliability of the network are realized.
Description
Technical field
The present invention relates to a kind of cognition network performance prediction method and device, relate in particular to a kind of cognition network performance prediction method and device based on the BP neural network.
Background technology
Along with the increase of miscellaneous service and the raising of customer requirements, network management will be a difficult problem with control.In order better to manage and Control Network, improve network service quality (QoS), introduced the notion of cognition network.
Cognition network is on the basis of cognitive radio, to propose, the notion of cognition connected from wireless single-hop extend to whole network, and be a kind of network with cognitive function.It can the sensing network situation and makes a strategic decision in view of the above, reasoning, learns and take appropriate action.Cognition network is attempted intelligent decision is incorporated in the network; Make network have ability from management, self study, self-optimizing; Can be when being desirably in change of network environment through reasoning, adaptive active is made a response, thereby really realize network may command, can manage, trusted.
Traditional network can only passive the making a response in back occur at environment change or fault (problem); The superiority of cognition network is that it can self-adaptation initiatively make a response in change of network environment; Can, network condition just take measures before degenerating, to prevent the appearance of this situation.Therefore, concerning next constantly the evaluation prediction of network condition for very important the cognition network.Through the network performance parameter that known parameter reasoning draws, be the important evidence of network state assessment, also be the basis that cognition network is made decisions on one's own.
Summary of the invention
Technical matters to be solved by this invention is to overcome the deficiency of prior art, and a kind of cognition network performance prediction method and device based on the BP neural network is provided, and has the more accurate prediction result.
The following technical scheme of the concrete employing of the present invention:
Based on the cognition network performance prediction method of BP neural network, the network parameter current according to cognition network predicted the cognition network performance parameter in the moment in future; This method as input, utilizes the BP neural network to carry out the cognition network performance prediction with the protocol stack parameter of cognition network and performance parameter; Said BP neural network obtains according to following method training:
Step 1, the protocol stack parameter and the performance parameter of gathering cognition network, and it is carried out normalization handle, training sample obtained;
Step 2, three layers of BP neural network of structure, wherein the input layer number is N-1, output layer node number is 1; N is the protocol stack parameter of the cognition network gathered and the species number of performance parameter; The number of hidden nodes is confirmed according to following method: the average with error sum of squares is the accuracy evaluation index, through loop test, chooses the number of hidden nodes that makes that the error sum of squares average is minimum;
Step 3, initial parameter are selected;
Step 4, utilize the training sample that obtains in the step 1 three layers of constructed BP neural network to be trained the BP neural network after obtaining training.
Said normalization processing is meant and all sample datas is transformed in the scope of (0,1) according to following formula:
Wherein,
and
is respectively maximal value and minimum value in the raw sample data;
is raw sample data,
be the numerical value after the conversion.
Said initial parameter comprises learning rate, initial weight, cycle index, and wherein the learning rate span is (0.01,0.8), and initial weight is chosen equally distributed random number between (1,1), and cycle index is confirmed by the loop test in the step 2.
Preferably, said protocol stack parameter comprises average queue length, end-to-end round-trip delay and tcp window size; Said performance parameter comprises time delay, shake, handling capacity, packet loss, number of dropped packets.
Said step 4 specifically comprises:
Step 401, will pass through pretreated sample and be input to the BP neural network;
Step 402, carry out the parameter initialization setting, comprise maximum frequency of training, training objective, the number of hidden nodes, initial weight, threshold value, initial learn speed;
Step 403, calculate the input and output value of each layer, calculate the error of each layer;
Step 404, according to the weights and the threshold value of adaptive learning rate algorithms correction neural network;
Step 405, as reaching training objective or frequency of training greater than preset maximum frequency of training, then stop training; As not, then return step 404.
Based on the cognition network performance prediction device of BP neural network, this device comprises by signal flow to the information sensing module, data preprocessing module, the prediction module that connect successively;
Said information sensing module is used for gathering in real time the protocol stack parameter and the performance parameter of cognition network, and with data transmission to data preprocessing module, said protocol stack parameter comprises average queue length, end-to-end round-trip delay and tcp window size; Said performance parameter comprises time delay, shake, handling capacity, packet loss, number of dropped packets;
The data that said data preprocessing module is carried out after normalization is handled and will be handled the data that receive are sent to prediction module; Said normalization processing is meant and all sample datas is transformed in the scope of (0,1) according to following formula:
Wherein,
and
is respectively maximal value and minimum value in the raw sample data;
is raw sample data,
be the numerical value after the conversion;
The data that said prediction module transmits data preprocessing module are as input, and good BP neural network is predicted to utilize training in advance, obtains the following network parameter of cognition network constantly; Said BP neural network obtains according to following method training:
Step 1, the protocol stack parameter and the performance parameter of gathering cognition network, and it is carried out normalization handle, training sample obtained;
Step 2, three layers of BP neural network of structure, wherein the input layer number is N-1, output layer node number is 1; N is the protocol stack parameter of the cognition network gathered and the species number of performance parameter; The number of hidden nodes is confirmed according to following method: the average with error sum of squares is the accuracy evaluation index, through loop test, chooses the number of hidden nodes that makes that the error sum of squares average is minimum;
Step 3, initial parameter are selected;
Step 4, utilize the training sample that obtains in the step 1 three layers of constructed BP neural network to be trained the BP neural network after obtaining training.
Compare prior art, the present invention has following beneficial effect:
(l) network protocol stack parameter and performance parameter are associated, when network condition changes, the reason that can very fast finding network is changed, thus important basis is provided for dealing with problems.
(2) the BP neural network structure is simple, adjustable parameter is many, training algorithm is many, can be handling good, the present invention is the basis with the self-learning property of BP neural network, through selecting suitable network parameter, can reach very high precision of prediction.
Description of drawings
Fig. 1 is the structural representation that the present invention is based on the cognition network performance prediction device of BP neural network;
Fig. 2 is the algorithm flow chart of BP neural metwork training among the present invention.
Embodiment
Below in conjunction with accompanying drawing technical scheme of the present invention is elaborated:
Cognition network performance prediction device based on the BP neural network of the present invention, its structure is as shown in Figure 1, comprises by signal flow to the information sensing module, data preprocessing module, the prediction module that connect successively.The course of work below in conjunction with this device is elaborated to Forecasting Methodology of the present invention.
The information sensing module is used for gathering in real time the network parameter of cognition network.The network parameter of being gathered among the present invention comprises protocol stack parameter and network performance parameter.Wherein performance parameter comprises time delay, shake, handling capacity, packet loss, number of dropped packets; The protocol stack parameter comprises average queue length, end-to-end round-trip delay and tcp window size.Above-mentioned network parameter can obtain through 100/1000M FE capture card, atm link capture card and SDH link capture card.
For avoiding the excessive network paralysis that causes of raw data, handle raw data.The present invention carries out normalization through pre-processing module to data and handles, and through the normalization formula all data conversions is arrived in the scope of (0,1).The normalization formula is as follows:
Wherein,
and
is respectively maximal value and minimum value in the sample data;
is raw sample data,
be the numerical value after the conversion.So not only avoid the input data to fall into the zone of saturation, also kept original characteristic of data.
The data that prediction module transmits data preprocessing module are as input, and good BP neural network is predicted to utilize training in advance, obtains the following network parameter of cognition network constantly; Said BP neural network obtains according to following method training:
Step 1, the protocol stack parameter and the performance parameter of gathering cognition network, and it is carried out normalization handle, training sample obtained.
Step 2, structure BP neural network.
The latent number of plies of the design packet includes network of network topology structure, input and output node layer number.This method adopts three layers of BP neural network, and the promptly latent number of plies is 1.The node number of input layer and output layer is by the parameter determining of gathering.If the parameter kind of gathering is N, then the input layer number is N-1, and output layer node number is 1.
Need confirm the number of hidden nodes then.
Select the reference formula of best the number of hidden nodes
as follows:
Wherein, m is the output neuron number, and n is the input block number, and a is the constant between [1,10].
The present invention with the average
of error sum of squares as the accuracy estimating index.The definition of error sum of squares average is following:
Definition: the expectation derived value of supposing the system is
; Corresponding network real output value is
; Reasoning error e is
, and then the formula of the average of error sum of squares
is:
Wherein,
is desired output, and
is the actual output of network.
At first calculate the scope of best the number of hidden nodes through the hidden node formula; Be the accuracy evaluation index with the error sum of squares average then; In the scope of having calculated, carry out loop test, choose suitable the number of hidden nodes and make the error sum of squares average minimum.
Step 3, initial parameter are selected.
Initial parameter comprises learning rate, initial weight, cycle index.In order to keep network stabilization and rate of convergence, the learning rate value is between (0.01,0.8).Initial weight is chosen and is obeyed equally distributed random number between (1,1), to improve the training speed of network.Cycle index is confirmed through the loop test in the step 2.
Step 4, utilize the training sample that obtains in the step 1 three layers of constructed BP neural network to be trained the BP neural network after obtaining training.
The present invention adopts the trainlm function that the BP neural network that makes up is trained; The learning algorithm of this function is the Levenberg-Marquadt back propagation algorithm; This algorithm is a kind of based on the theoretical training algorithm of numerical optimization, and learning rate is adaptive, therefore; The speed of convergence of function trainlm is very fast, and the training error of network is also less.
Exist corresponding funtcional relationship between the various network parameters.The present invention adopts the BP neural network to describe the funtcional relationship of this implicit expression between protocol stack parameter and the network performance parameter; Under the situation that network structure has been confirmed; Through seeking the weights of an appropriate, make network approach the funtcional relationship between protocol stack parameter and the performance parameter as much as possible.Training sample is offered the BP neural network, and neuronic activation value is propagated to output layer through each middle layer from input layer, obtains the input response of network at each neuron of output layer.Then,, oppositely get back to input layer, respectively connect weights thereby successively revise through each middle layer from output layer according to the direction that reduces error between target output and the actual output.Concrete training step is following:
Step 401, will pass through pretreated sample and be input to the BP neural network;
Step 402, carry out the parameter initialization setting, comprise maximum frequency of training Max_epoch, error precision
(being also referred to as training objective), the number of hidden nodes, initial weight, threshold value, initial learn speed;
Step 403, calculate the input and output value of each layer, calculate the error of each layer;
Step 404, according to the weights and the threshold value of adaptive learning rate algorithms correction neural network;
Step 405, if error precision greater than
, then return step 404; If error precision less than
or frequency of training during greater than its maximum frequency of training Max_epoch, stops training.
The BP neural network that trains has been described the funtcional relationship of implicit expression between protocol stack parameter and the network performance parameter.With network protocol stack parameter and the network performance parameter fan-in network gathered, utilize the stable network structure (comprising training parameter), connection weights and the threshold values that have obtained to the test sample book analysis, dope following network performance parameter constantly.Thereby can initiatively make a policy according to the network condition that is doped, satisfy user's demand with maximization.
Claims (7)
1. based on the cognition network performance prediction method of BP neural network, the network parameter current according to cognition network predicted the cognition network performance parameter in the moment in future; It is characterized in that this method as input, utilizes the BP neural network to carry out the cognition network performance prediction with the protocol stack parameter of cognition network and performance parameter; Said BP neural network obtains according to following method training:
Step 1, the protocol stack parameter and the performance parameter of gathering cognition network, and it is carried out normalization handle, training sample obtained;
Step 2, three layers of BP neural network of structure, wherein the input layer number is N-1, output layer node number is 1; N is the protocol stack parameter of the cognition network gathered and the species number of performance parameter; The number of hidden nodes is confirmed according to following method: the average with error sum of squares is the accuracy evaluation index, through loop test, chooses the number of hidden nodes that makes that the error sum of squares average is minimum;
Step 3, initial parameter are selected;
Step 4, utilize the training sample that obtains in the step 1 three layers of constructed BP neural network to be trained the BP neural network after obtaining training.
2. according to claim 1 based on the cognition network performance prediction method of BP neural network, it is characterized in that said protocol stack parameter comprises average queue length, end-to-end round-trip delay and tcp window size; Said performance parameter comprises time delay, shake, handling capacity, packet loss, number of dropped packets.
3. according to claim 1 based on the cognition network performance prediction method of BP neural network, it is characterized in that said normalization processing is meant and all sample datas is transformed in the scope of (0,1) according to following formula:
Wherein,
and
is respectively maximal value and minimum value in the raw sample data;
is raw sample data,
be the numerical value after the conversion.
4. according to claim 1 based on the cognition network performance prediction method of BP neural network; It is characterized in that said initial parameter comprises learning rate, initial weight, cycle index, wherein the learning rate span is (0.01; 0.8); Initial weight is chosen equally distributed random number between (1,1), and cycle index is confirmed by the loop test in the step 2.
5. according to claim 1 based on the cognition network performance prediction method of BP neural network, it is characterized in that said step 4 specifically comprises:
Step 401, will pass through pretreated sample and be input to the BP neural network;
Step 402, carry out the parameter initialization setting, comprise maximum frequency of training, training objective, the number of hidden nodes, initial weight, threshold value, initial learn speed;
Step 403, calculate the input and output value of each layer, calculate the error of each layer;
Step 404, according to the weights and the threshold value of adaptive learning rate algorithms correction neural network;
Step 405, as reaching training objective or frequency of training greater than preset maximum frequency of training, then stop training; As not, then return step 404.
6. according to claim 1 based on the cognition network performance prediction method of BP neural network, it is characterized in that, the BP neural network is trained adopt the Levenberg-Marquadt back propagation algorithm.
7. based on the cognition network performance prediction device of BP neural network, it is characterized in that this device comprises by signal flow to the information sensing module, data preprocessing module, the prediction module that connect successively;
Said information sensing module is used for gathering in real time the protocol stack parameter and the performance parameter of cognition network, and with data transmission to data preprocessing module, said protocol stack parameter comprises average queue length, end-to-end round-trip delay and tcp window size; Said performance parameter comprises time delay, shake, handling capacity, packet loss, number of dropped packets;
The data that said data preprocessing module is carried out after normalization is handled and will be handled the data that receive are sent to prediction module; Said normalization processing is meant and all sample datas is transformed in the scope of (0,1) according to following formula:
Wherein,
and
is respectively maximal value and minimum value in the raw sample data;
is raw sample data,
be the numerical value after the conversion;
The data that said prediction module transmits data preprocessing module are as input, and good BP neural network is predicted to utilize training in advance, obtains the following network parameter of cognition network constantly; Said BP neural network obtains according to following method training:
Step 1, the protocol stack parameter and the performance parameter of gathering cognition network, and it is carried out normalization handle, training sample obtained;
Step 2, three layers of BP neural network of structure, wherein the input layer number is N-1, output layer node number is 1; N is the protocol stack parameter of the cognition network gathered and the species number of performance parameter; The number of hidden nodes is confirmed according to following method: the average with error sum of squares is the accuracy evaluation index, through loop test, chooses the number of hidden nodes that makes that the error sum of squares average is minimum;
Step 3, initial parameter are selected;
Step 4, utilize the training sample that obtains in the step 1 three layers of constructed BP neural network to be trained the BP neural network after obtaining training.
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