A Development of Cascade Granular Neural Networks

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概要

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• This paper studies the design of Cascade Granular Neural Networks (CGNN) for human-centric systems. In contrast to typical rule-based systems encountered in fuzzy modeling, the proposed method consists of two-phase development for CGNN. First, we construct a Granular Neural Network (GNN) which could be treated as a preliminary design. Next, all modeling discrepancies are compensated by a second GNN with a collection of rules that become attached to the regions of the input space where the error is localized. These granular networks are constructed by building a collection of user-centric information granules through Context-based Fuzzy c-Means (CFCM) clustering. Finally, the experimental results on two examples reveal that the proposed approach shows good performance in comparison with the previous works.

• 2011-07-01

著者

• Kwak Keun-chang

  Dept. Of Control Instrumentation And Robot Eng. Chosun University

関連論文

• Adaptive Neuro-Fuzzy Networks with the Aid of Fuzzy Granulation(Biocybernetics, Neurocomputing)
• A Development of Cascade Granular Neural Networks
• A Design of Genetically Optimized Linguistic Models

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