Performance-enhancing bifurcations in a self-organising neural network

Authors:
Terence Kwok;Kate A. Smith
Affiliations:
School of Business Systems, Faulty of Information Technology, Monash University, Clayton, Victoria, Australia;School of Business Systems, Faulty of Information Technology, Monash University, Clayton, Victoria, Australia
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

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Abstract

In large number of real world dilemmas and applications, especially in industrial areas, efficient processing of the data is a chief condition to solve problems. The constraints relative to the nature ol'data to be processed, difficult dilemma lated to the choice of appropriated processing techniques and allied parameters make complexity reduction a key point on both data and processing levels. In this paper we present an ANN based data driven treelike Multiple Model generator, that we called T-DTS (Treelike Divide To Simplify), able to reduce complexity on both data and processing levels. The eficiency of such approach has been analyzed trough applications dealing with none-linear process identification. Experiinental results validating our approach are reported and discussed.