Study on RBF Neural Network Based on Gray Wolf Optimization Algorithm

GUO Zhen-zhou; LIU Ran; GONG Chang-qing; ZHAO Liang

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Study on RBF Neural Network Based on Gray Wolf Optimization Algorithm

更新时间：2026-09-04

- Study on RBF Neural Network Based on Gray Wolf Optimization Algorithm
- Vol. 34, Issue 7, Pages: 7-10(2017)
- 作者机构：
  
  沈阳航空航天大学机构计算机学院,辽宁,沈阳,110136
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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郭振洲, 刘然, 拱长青, 赵亮. 基于改进灰狼算法的RBF神经网络研究[J]. 微电子学与计算机, 2017, 34(7): 7-10, 17.

GUO Zhen-zhou, LIU Ran, GONG Chang-qing, et al. Study on RBF Neural Network Based on Gray Wolf Optimization Algorithm[J]. 2017, 34(7): 7-10.
郭振洲, 刘然, 拱长青, 赵亮. 基于改进灰狼算法的RBF神经网络研究[J]. 微电子学与计算机, 2017, 34(7): 7-10, 17. DOI：

GUO Zhen-zhou, LIU Ran, GONG Chang-qing, et al. Study on RBF Neural Network Based on Gray Wolf Optimization Algorithm[J]. 2017, 34(7): 7-10. DOI：

摘要

对于RBF神经网络权值参数优化问题

提出一种基于改进灰狼优化算法的RBF神经网络的方法.针对灰狼算法的收敛精度低的缺点提出一种非线性收敛的灰狼算法

将隐层到输出层的权值矩阵映射成灰狼算法里的人工狼

利用灰狼优化算法收敛速度快

全局搜索能力强的特点对RBF网络的隐层到输出层的权值进行优化.本文采用KDD CUP99数据集进行实验

实验结果表明: 本文所提出的算法具有更好的分类检测效果具有更好的检测分类效果

并在一定程度上增强了RBF神经网络对非线性问题的处理能力.

Abstract

For the problem of RBF parameter optimization neural network weights

and presents a method of RBF neural network optimization algorithm based on the improvement gray wolf. A nonlinear algorithm for convergence of the proposed algorithm convergence precision of gray wolf low.The hidden layer to the output layer weights matrix mapping algorithm in artificial to wolf

wolf using optimization algorithm has fast convergence speed and global search ability of the hidden layer of RBF network to the output layer weights are optimized to improve the RBF neural network. This paper uses the KDD CUP 99 data set for experiment

the experimental results show that the effect of classification results the proposed algorithm has better better detection and classification

and enhance the processing ability of the RBF neural network for nonlinear problems in a certain extent.

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references

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