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Proceedings Paper

Various ways for building a multi-neural network system: application to a control process
Author(s): Abdennasser Chebira; Kurosh Madani; Gilles Mercier
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Paper Abstract

The use of neurocomputing to solve real world problems is some times penalized by the long computing time, especially in the training phase of the neural network. A lot of approaches were suggested in the literature that allows to reduce the computing time and to enhance the generalization factor. The approach we propose in this paper is original by the fact that it is based on the following paradigm: Divide To Simplify or DTS. The task of learning is decomposed in 2 steps. In the first step, we analyze the set of the input patterns and decompose the input space in several regions of interest. This is done by using an unsupervised prototype based neural network: a Kohonen Self Organized feature Map (SOM). The second step consists of training several feature based networks to learn the behavior of each region of interest. In this way, we obtain a set of specialized neural networks. We will present in this paper various data driven methods that use the DTS paradigm to build efficient Multi- Neural Networks systems. The efficiency of this context means fast learning time, fast execution or relaxation time, enhanced generalization factor and intrinsic implementation on a parallel machine. Our approach has been used, with success, in an industrial real world process control to analyze such a complex problem with multi-variable data.

Paper Details

Date Published: 4 April 1997
PDF: 12 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271475
Show Author Affiliations
Abdennasser Chebira, Univ. Paris XII (France)
Kurosh Madani, Univ. Paris XII (France)
Gilles Mercier, Univ. Paris XII (France)


Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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