个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:吉林工业大学
学位:硕士
所在单位:控制科学与工程学院
电子邮箱:jianhuay@dlut.edu.cn
The convergence analysis of SpikeProp algorithm with smoothing L1∕2 regularization.
点击次数:
论文类型:期刊论文
发表时间:2018-01-01
发表刊物:Neural networks : the official journal of the International Neural Network Society
收录刊物:SCIE
卷号:103
页面范围:19-28
ISSN号:1879-2782
关键字:Spiking neural networks; SpikeProp; Smoothing L-1/2 regularization; Convergence; Sparsity
摘要:Unlike the first and the second generation artificial neural networks, spiking neural networks (SNNs) model the human brain by incorporating not only synaptic state but also a temporal component into their operating model. However, their intrinsic properties require expensive computation during training. This paper presents a novel algorithm to SpikeProp for SNN by introducing smoothing L1∕2 regularization term into the error function. This algorithm makes the network structure sparse, with some smaller weights that can be eventually removed. Meanwhile, the convergence of this algorithm is proved under some reasonable conditions. The proposed algorithms have been tested for the convergence speed, the convergence rate and the generalization on the classical XOR-problem, Iris problem and Wisconsin Breast Cancer classification. Copyright © 2018 Elsevier Ltd. All rights reserved.