An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications

Shih Hung Yang*, Yon-Ping Chen

*此作品的通信作者

研究成果: Article同行評審

54 引文 斯高帕斯(Scopus)

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.

原文English
頁(從 - 到)140-149
頁數10
期刊Neurocomputing
86
DOIs
出版狀態Published - 1 六月 2012

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