RBF neural network optimization method based on improved GWO algorithm

A neural network and optimization method technology, applied in the field of neural network optimization, can solve the problems of slow convergence speed of GWO algorithm, easy to ignore the surrounding optimal solution information, and fall into local optimum, so as to reduce adverse effects, speed up convergence speed, increase The effect of precision

Pending Publication Date: 2020-09-25
JIANGSU UNIV OF SCI & TECH
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Problems solved by technology

[0007] The purpose of the present invention is to solve the technical problems that the existing GWO algorithm has a slow convergence speed, and the later search range is small, it is easy to ignore the surrounding optimal solution information, and it is easy to fall into a local optimal technical problem. On this basis, an improved GWO algorithm is proposed. RBF neural network optimization method

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  • RBF neural network optimization method based on improved GWO algorithm
  • RBF neural network optimization method based on improved GWO algorithm
  • RBF neural network optimization method based on improved GWO algorithm

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[0054] Exemplary embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the shown drawings and described specific implementation methods are only exemplary, and are intended to illustrate the application principle of the present invention, not to limit the application scope of the present invention.

[0055] The invention discloses an RBF neural network optimization method based on the improved GWO algorithm, figure 2 A schematic diagram of the optimization of the GWO algorithm is given. image 3 The topology structure of the RBF neural network is given, and the sea clutter prediction model of the RBF neural network is taken as an example to illustrate, figure 1 The specific implementation steps of this example are given:

[0056] Step 1: Determine the network topology. The network parameters that need to be optimized, including data center parameters, data width parameters and network weight pa...

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Abstract

The invention belongs to the technical field of neural network optimization, and tarticularly relates to an RBF neural network optimization method based on an improved GWO algorithm. A grey wolf population is divided into two sub-populations by setting a threshold value, different search strategies are executed respectively, an improved GWO optimization algorithm is used for searching for optimalinitial parameters of an RBF neural network, a sea clutter prediction model of the RBF neural network is established, and sea clutters are predicted and suppressed. According to the invention, the fitness mean value of each generation of population is calculated; the fitness threshold value is dynamically set, the grey wolf with the fitness higher than the threshold value executes the strategy oflarge-range search, and otherwise, the grey wolf executes the strategy of small-range search, so that each generation of population has global search and local search capabilities, and the convergencerate of the GWO algorithm and the precision of later optimization are improved. The improved GWO optimization algorithm is used for optimizing the initial parameters of the RBF neural network, and the stability and precision of the network are further improved.

Description

technical field [0001] The invention belongs to the technical field of neural network optimization, and specifically relates to an RBF neural network optimization method based on an improved GWO algorithm. Background technique [0002] In the military and civilian fields, radar has become an essential part. When radar is used to detect sea targets, there will often be a large amount of sea clutter superimposed on the echo signal. The existence of sea clutter brings great challenges to the effective detection of sea targets. The accurate prediction and Suppression becomes an essential step in maritime object detection. [0003] Scholars' early research on sea clutter started from its statistical characteristics, but neither the classic statistical characteristic model of sea clutter nor its improved model can accurately describe sea clutter, and there is no universal statistical characteristic model for different sea conditions . Further studies have found that sea clutter...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08G06N3/00G06N3/04G06K9/62
CPCG06N3/082G06N3/006G06N3/0418G06N3/045G06F18/23213
Inventor 何康宁尚尚王召斌刘明杨童李维燕李朕陈康宁
Owner JIANGSU UNIV OF SCI & TECH
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