NEUROCOMPUTER BASED COMPLEXITY ESTIMATOR OPTIMIZING A HYBRID MULTI NEURAL NETWORK STRUCTURE | Zendy

Ivan Budnyk | Zendy; El khier Bouyoucef | Zendy; Abdennasser Chebira | Zendy; Kurosh Madani | Zendy

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NEUROCOMPUTER BASED COMPLEXITY ESTIMATOR OPTIMIZING A HYBRID MULTI NEURAL NETWORK STRUCTURE

Author(s) -

Ivan Budnyk,

El khier Bouyoucef,

Abdennasser Chebira,

Kurosh Madani

Publication year - 2014

Publication title -

international journal of 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.7.3.533

Subject(s) - computer science , estimator , ibm , generalization , artificial neural network , computational complexity theory , tree (set theory) , machine learning , software , divide and conquer algorithms , artificial intelligence , theoretical computer science , data mining , algorithm , operating system , statistics , mathematics , mathematical analysis , materials science , nanotechnology

This paper presents application of ZISC© IBM® neurocomputer based approach for estimating task complexity within T-DTS framework. T-DTS (Tree-like Divide To Simplify) is Hybrid Multiple Neural Networks software platform which constructs a neural tree structures of a complex problem following the paradigm “divide” and “conquer”. Complexity estimator modules are the core of this framework. One of them is ZISC© IBM® complexity estimator that has been recently applied to T-DTS. The global aim of this research work is to increase T-DTS performance in terms of generalization and learning abilities. In this paper we demonstrate matchless ZISC© IBM® based neurocomputer complexity estimator effect on database decomposition and searching for optimal T-DTS adjustment of complexity threshold.

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