Various ways for building a multi-neural network system: application to a control process

Abdennasser Chebira; Kurosh Madani; Gilles Mercier

doi:10.1117/12.271475

4 April 1997 Various ways for building a multi-neural network system: application to a control process

Abdennasser Chebira, Kurosh Madani, Gilles Mercier

Proceedings Volume 3077, Applications and Science of Artificial Neural Networks III; (1997) https://doi.org/10.1117/12.271475
Event: AeroSense '97, 1997, Orlando, FL, United States

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.

Citation Download Citation

Abdennasser Chebira, Kurosh Madani, and Gilles Mercier "Various ways for building a multi-neural network system: application to a control process", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271475

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