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MULTIPLE NEURAL NETWORK MODELS GENERATOR WITH COMPLEXITY ESTIMATION AND SELF-ORGANIZATION ABILITIES
Author(s) -
El-Khier El-Khier Bouyoucef,
Abdennasser Chebira,
Mariusz Rybnik,
Kurosh Madani
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.4.3.358
Subject(s) - computer science , modular design , artificial neural network , artificial intelligence , reduction (mathematics) , set (abstract data type) , task (project management) , computational complexity theory , generator (circuit theory) , information processing , machine learning , algorithm , power (physics) , mathematics , engineering , physics , geometry , systems engineering , quantum mechanics , neuroscience , biology , programming language , operating system
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.

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