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Tree-Like Multiple Neural Network Models Generator with a Complexity Estimation Based Decomposer

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4 Author(s)
Madani, K. ; Intell. in Instrum. & Syst. Div., Paris Univ., Paris ; Chebira, A. ; Rybnik, M. ; Bouyoucef, E.-K.

In this article we present a self-organizing hybrid modular approach that is aimed at reduction of processing task complexity by decomposition of an initially complex problem into a set of simpler sub-problems. This approach hybridizes artificial neural networks based artificial intelligence and complexity estimation loops in order to reach a higher level intelligent processing capabilities. In consequence, our approach mixtures learning, complexity estimation and specialized data processing modules in order to achieve a higher level self-organizing modular intelligent information processing system. Experimental results validating the presented approach are reported and discussed..

Published in:

Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2005. IDAACS 2005. IEEE

Date of Conference:

5-7 Sept. 2005