T-S Fuzzy Neural Network Parameter Optimization Based on Ant Colony Algorithm

被引：0

作者：

Li, Qinglu ^{[1
]}

Zhang, Jinghua ^{[1
]}

Zhao, Qun

机构：

[1] Lanzhou Univ Technol, Dept Engn Mech, Lanzhou 730050, Gansu, Peoples R China

来源：

PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON MECHANICAL ENGINEERING AND MECHANICS, VOLS 1 AND 2 | 2009年

关键词：

T-S fuzzy model; Parameter optimization; Fuzzy neural network; Ant colony algorithm; Least squares method; SYSTEMS;

D O I：

暂无

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Usually, the accuracy of fuzzy modeling is low using experiment dada. In order to improve modeling accuracy of T-S fuzzy model, T-S fuzzy neural network optimization method is proposed based on ant colony algorithm. Gradient descent method is lack of global superiority, at the same time the initial conditions have significant effect on convergence. But ant colony algorithm is holding global superiority. So, in this paper, the ant colony algorithm is used to determine the learning initial value of the fuzzy neural network, and then combined with least square method and gradient descent method to optimize parameters. The simulation results show that the proposed method is effective, aiming to improve the modeling accuracy.

引用

页码：659 / 662

页数：4

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