MULTIPLE NEURAL NETWORK MODELS GENERATOR WITH COMPLEXITY ESTIMATION AND SELF-ORGANIZATION ABILITIES
DOI:
https://doi.org/10.47839/ijc.4.3.358Keywords:
Artificial Neural Networks, Complexity Estimation, Self-Organization, Intelligent Decomposer, Universal Information processingAbstract
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.References
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