Design of Sparse Span-lateral Inhibition Neural Network Based on Connection Self-organization Development

被引：0
|
作者：
Yang G. [1 ,2 ]
Wang L. [1 ,2 ]
Dai L.-Z. [1 ,2 ]
Yang H. [1 ,2 ]
机构：
[1] School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang
[2] Key Laboratory of Advanced Control and Optimization of Jiangxi Province, Nanchang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2019年 / 45卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Intelligence; Small-world network; Span-lateral inhibition neural network (S-LINN); Spares;
D O I：
10.16383/j.aas.2018.c170374
中图分类号：
学科分类号：
摘要：
Inspired by the sparse connection of neurons in biological nervous system and the relationship between children and adolescents' intellectual ability and cortical development, a connection self-organization development-based sparse span-lateral inhibition neural network (sS-LINN) is developed to solve the structure adjustment and parameter learning problem, which adopts the small-world network connection mode as the initial sparse network architecture. A growing-pruning rule of network connection is designed to adjust and control the sparseness of network connections based on the definitions of connection sparseness and neuron output contribution rate. Performance of the proposed sparse S-LINN is evaluated successfully through simulation using nonlinear dynamic system identification and function approximation benchmark problems. It is shown that the proposed sS-LINN can produce a very compact structure with good generalization ability in comparison with other methods. Copyright © 2019 Acta Automatica Sinica. All rights reserved.
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页码：808 / 818
页数：10
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