Applying RBF Neural Networks and Genetic Algorithms to Nonlinear System Optimization

被引：3

作者：

Wang, Hongfa ^{[1
]}

Xu, Xinai ^{[2
]}

机构：

[1] Zhejiang Water Conservancy & Hydropower Coll, Hangzhou 310018, Zhejiang, Peoples R China

[2] Jiangxi Educ Coll, Nanchang 330029, Jiangxi, Peoples R China

来源：

ADVANCED DESIGNS AND RESEARCHES FOR MANUFACTURING, PTS 1-3 | 2013年 / 605-607卷

关键词：

RBF Neural Network; Genetic Algorithm; Nonlinear System; Optimization;

D O I：

10.4028/www.scientific.net/AMR.605-607.2457

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Nonlinear system optimization is always an issue that needs to be considered in engineering practices and management. In order to obtain optimal solutions without analysis formulas to nonlinear systems, we first construct a radial-base-function (RBF) neural network using the newrb() function in MALTAB 7.0, then train the neural network according to input and output, and finally obtain the solution using a genetic algorithm. Simulated experimental results show that the proposed algorithm is able to achieve optimal solutions with a relatively fast speed of convergence.

引用

页码：2457 / +

页数：2

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