MULTIPLE NEURAL NETWORK MODELS GENERATOR WITH COMPLEXITY ESTIMATION AND SELF-ORGANIZATION ABILITIES
El-khier Bouyoucef, Abdennasser Chebira, Mariusz Rybnik, Kurosh Madani · International Journal of Computing · 2014
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.