NEUROCOMPUTER BASED COMPLEXITY ESTIMATOR OPTIMIZING A HYBRID MULTI NEURAL NETWORK STRUCTURE
DOI:
https://doi.org/10.47839/ijc.7.3.533Keywords:
ZISC© IBM® Neurocomputer, T-DTS, Hybrid Multiply Neural Networks, Self Organizing Map – Linear Support Vector Machine Decision Tree, RBF AlgorithmAbstract
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.References
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