標題: An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications
作者: Yang, Shih-Hung
Chen, Yon-Ping
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Evolutionary algorithm;Neural network;Constructive;Pruning;Prediction
公開日期: 1-六月-2012
摘要: We propose a method for designing artificial neural networks (ANNs) for prediction problems based on an evolutionary constructive and pruning algorithm (ECPA). The proposed ECPA begins with a set of ANNs with the simplest possible structure, one hidden neuron connected to an input node, and employs crossover and mutation operators to increase the complexity of an ANN population. Additionally, cluster-based pruning (CBP) and age-based survival selection (ABSS) are proposed as two new operators for ANN pruning. The CBP operator retains significant neurons and prunes insignificant neurons on a probability basis and therefore prevents the exponential growth of an ANN. The ABSS operator can delete old ANNs with potentially complex structures and then introduce new ANNs with simple structures; thus, the ANNs are less likely to be trapped in a fully connected topology. The ECPA framework incorporates constructive and pruning approaches in an attempt to efficiently evolve compact ANNs. As a demonstration of the method, ECPA is applied to three prediction problems: the Mackey-Glass time series, the number of sunspots, and traffic flow. The numerical results show that ECPA makes the design of ANNs more feasible and practical for real-world applications. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/11536/15963
ISSN: 0925-2312
期刊: NEUROCOMPUTING
Volume: 86
Issue: 
結束頁: 140
顯示於類別:期刊論文


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  1. 000303428700013.pdf