A NEURAL-BASED NONLINEAR L1-NORM OPTIMIZATION ALGORITHM FOR DIAGNOSIS OF NETWORKS*

被引：0
|
作者：
He Yigang (Department of Electrical Engineering
机构：
来源：
Journal of Electronics(China) | 1998年 / 04期
基金：
中国国家自然科学基金;
关键词：
Fault diagnosis; L1-norm; Neural optimization;
D O I：
暂无
中图分类号：
TP183 [人工神经网络与计算];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on exact penalty function, a new neural network for solving the L1-norm optimization problem is proposed. In comparison with Kennedy and Chua’s network(1988), it has better properties.Based on Bandler’s fault location method(1982), a new nonlinearly constrained L1-norm problem is developed. It can be solved with less computing time through only one optimization processing. The proposed neural network can be used to solve the analog diagnosis L1 problem. The validity of the proposed neural networks and the fault location L1 method are illustrated by extensive computer simulations.
引用
收藏
页码：365 / 371
页数：7
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