Open AccessNEUROCOMPUTER 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 -
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.