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Genetic algorithm and Kalman filtering based RBFN (Radial Basis Function Networks) combined training method

A Kalman filter and genetic algorithm technology, applied in the field of RBFN combined training, can solve the problems of inability to adaptively correct the network center value and weight value, easy to fall into local minimum points, slow training speed, etc.

Inactive Publication Date: 2013-12-25
BEIHANG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a kind of RBFN combined training method based on genetic algorithm and Kalman filter, it will overcome the limitation based on the neural network of traditional algorithm, and training speed is relatively slow; Small point; cannot adaptively correct problems such as network center value and weight

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  • Genetic algorithm and Kalman filtering based RBFN (Radial Basis Function Networks) combined training method
  • Genetic algorithm and Kalman filtering based RBFN (Radial Basis Function Networks) combined training method
  • Genetic algorithm and Kalman filtering based RBFN (Radial Basis Function Networks) combined training method

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Embodiment Construction

[0057] 1. RBFN neural network mathematical model

[0058] The RBFN neural network is a special three-layer forward neural network, which completes the mapping from the m-dimensional input space to the n-dimensional output space. RBFN neural network structure such as figure 1 shown.

[0059] The radial basis function chooses the inverse multivariate function:

[0060] g(v)=(v 2 +β 2 ) -1 / 2 (1)

[0061] v=||x-v i || 2 ,i=1,2...,c,v i is the center vector of the radial basis function.

[0062] The network input is an m-dimensional vector x=(x 1 ,x 2 ,...,x m ) T , the output is n as a vector y=(y 1 ,y 2 ,...,y n ) T . After the input vector is transformed by radial basis function, its output is weighted and then passed to the output layer.

[0063] for figure 1 Given the RBFN network structure diagram, the weight matrix is ​​expressed as:

[0064] w 01 ...

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Abstract

The invention relates to a genetic algorithm and Kalman filtering based RBFN (Radial Basis Function Networks) combined training method which comprises the following five steps: I, setting a random initialized population according to parameters to code central values: v11, v12,..., v1m, v21,..., and vcm; II, calculating adaptive values of individuals in the population and storing the optimal adaptive value, wherein the target of training RBFN is to minimize an output error E and a fitness function is set as follows: Fit(fi)=1 / E; III, if set evolutionary algebra is realized or a current optimal individual satisfies the condition, returning network parameters and skipping to the step IV, otherwise, skipping to the step II after selection, crossing and genetic variation operation; IV, correcting the network parameters in a self-adaptive manner by a Kalman filtering algorithm, wherein the network parameter value optimized by a genetic algorithm is taken as the network initial parameter of the Kalman filtering algorithm; and V, ending a program when the maximum iterative time limitation is realized or the current network error meets the requirement, otherwise, skipping to the step 4 to operate continuously.

Description

technical field [0001] The invention relates to an RBFN combined training method based on a genetic algorithm and a Kalman filter, and belongs to the technical fields of artificial neural network technology, pattern recognition and artificial intelligence. Background technique [0002] As a concentrated application of cutting-edge technology, spacecraft condenses a lot of valuable intelligence, financial resources, manpower, and material resources. For highly complex and nonlinear systems, such as spacecraft fault detection and identification, etc., when the mechanism is unknown or the mathematical model is difficult to establish, the neural network can exert its own nonlinearity, high fault tolerance, and can handle complex patterns, parallel computing and association. With unique advantages such as memory, it solves many pattern recognition problems that are difficult to solve or poorly solved by traditional methods. [0003] Radial-Basis Function Neural Network (RBFN) ha...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12
Inventor 赵琦周贞贞冯文全
Owner BEIHANG UNIV
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