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Evolutionary computation for dynamic parameter optimisation of evolving connectionist systems for on-line prediction of time series with changing dynamics

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3 Author(s)
Kasabov, N. ; Knowledge Eng. & Discovery Res. Inst., Auckland Univ. of Technol., New Zealand ; Qun Song ; Nishikanawa, I.

The paper describes a method of using evolutionary computation technique for parameter optimisation of evolving connectionist systems (ECOS) that operate in an online, life-long learning mode. ECOS evolve their structure and functionality from an incoming stream of data in either a supervised-, of/and in an unsupervised mode. The algorithm is illustrated on a case study of predicting a chaotic time-series that changes its dynamics over time. With the on-line parameter optimisation of ECOS, a faster adaptation and a better prediction is achieved. The method is practically applicable for real time applications.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:1 )

Date of Conference:

20-24 July 2003

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