Training neural networks with a multi-objective sliding mode control algorithm

被引：22
|
作者：
Costa, MA [1 ]
Braga, AP [1 ]
Menezes, BR [1 ]
Teixeira, RA [1 ]
Parma, GG [1 ]
机构：
[1] Univ Fed Minas Gerais, Dept Elect Engn, BR-30161970 Belo Horizonte, MG, Brazil
来源：
NEUROCOMPUTING | 2003年 / 51卷
关键词：
neural networks; multi-objective optimization; generalization;
D O I：
10.1016/S0925-2312(02)00697-5
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a new sliding mode control algorithm that is able to guide the trajectory of a multi-layer perceptron within the plane formed by the two objective functions: training set error and norm of the weight vectors. The results show that the neural networks obtained are able to generate an approximation to the Pareto set, from which an improved generalization performance model is selected. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：467 / 473
页数：7
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