Fault Tolerant Broad Learning System

被引：1

作者：

Adegoke, Muideen ^{[1
]}

Leung, Chi-Sing ^{[1
]}

Sum, John ^{[2
]}

机构：

[1] City Univ Hong Kong, Dept Elect Engn, Kowloon Tong, Hong Kong, Peoples R China

[2] Natl Chung Hsing Univ, Inst Technol Management, Taichung, Taiwan

来源：

NEURAL INFORMATION PROCESSING (ICONIP 2019), PT IV | 2019年 / 1142卷

关键词：

Broad learning system; Fault tolerance; Multiplicative noise; RBF NETWORKS; WEIGHT;

D O I：

10.1007/978-3-030-36808-1_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The broad learning system (BLS) approach provides low computational complexity solutions for training flat structure feedforward networks. However, many BLS algorithms deal with the faultless situation only. This paper addresses the fault tolerant ability of BLS networks. We call our approach fault tolerant BLS (FTBLS). First, we develop a fault tolerant objective function for BLS. Based on the developed objective function, we develop a training algorithm to construct a BLS network. The simulation results show that our proposed FTBLS is much better than the classical BLS.

引用

页码：95 / 103

页数：9

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