Parameter Tuning of MLP Neural Network Using Genetic Algorithms

被引：0

作者：

Er, Meng Joo ^{[1
]}

Liu, Fan ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

SIXTH INTERNATIONAL SYMPOSIUM ON NEURAL NETWORKS (ISNN 2009) | 2009年 / 56卷

关键词：

Genetic algorithms; Backpropagation; Function approximation; Nonlinear dynamic system identification; BUSINESS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a hybrid learning algorithm for a Multilayer Percept-rons (MLP) Neural Network using Genetic Algorithms (GA) is proposed. This hybrid learning algorithm has two steps: First, all the parameters (weights and biases) of the initial neural network are encoded to form a long chromosome and tuned by the GA. Second, as a result of the GA process, a quasi-Newton method called BFGS method is applied to train the neural network. Simulation studies on function approximation and nonlinear dynamic system identification are presented to illustrate the performance of the proposed learning algorithm.

引用

页码：121 / 130

页数：10

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