Pruning based robust backpropagation training algorithm for RBF network tracking controller

被引：2

作者：

Ni, Jie ^{[1
]}

Song, Qing ^{[1
]}

机构：

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

来源：

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS | 2007年 / 48卷 / 03期

关键词：

convergence; dead zone; pruning; robust backpropagation; tracking controller;

D O I：

10.1007/s10846-006-9093-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A pruning based robust backpropagation training algorithm is proposed for the online tuning of the Radial Basis Function(RBF) network tracking control system. The structure of the RBF network controller is derived using a filtered error approach. The proposed method in this paper begins with a relatively large network, and certain neural units of the RBF network are dropped by examining the estimation error increment. A complete convergence proof is provided in the presence of disturbance.

引用

页码：375 / 396

页数：22

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