A new ART-counterpropagation neural network for solving a forecasting problem

Tzu Chiang Liu*, Rong-Kwei Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer). ART-CPN involves real-time learning and is capable of developing a more stable and plastic prediction model of input patterns by self-organization. The advantages of ART-CPN include the ability to cluster, learn and construct the network model for forecasting problems. The network was applied to solve the real forecasting problems. The learning algorithm revealed better learning efficiency and good prediction performance.

Original languageEnglish
Pages (from-to)21-27
Number of pages7
JournalExpert Systems with Applications
Volume28
Issue number1
DOIs
StatePublished - 1 Jan 2005

Keywords

  • Adaptive resonance theory
  • Counterpropagation
  • Neural network

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