Adaptive Neuro-Fuzzy Networks with the Aid of Fuzzy Granulation(Biocybernetics, Neurocomputing)

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概要

• 論文の詳細を見る

• In this paper, we present the method for identifying an Adaptive Neuro-Fuzzy Networks (ANFN) with Takagi-Sugeno-Kang (TSK) fuzzy type based on fuzzy granulation. We also develop a systematic approach to generating fuzzy if-then rules from a given input-output data. The proposed ANFN is designed by the use of fuzzy granulation realized via context-based fuzzy clustering. This clustering technique builds information granules in the form of fuzzy sets and develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. The experimental results reveal that the proposed model yields a better performance in comparison with Linguistic Models (LM) and Radial Basis Function Networks (RBFN) based on context-based fuzzy clustering introduced in the previous literature for Box-Jenkins gas furnace data and automobile MPG prediction.

• 社団法人電子情報通信学会の論文
• 2005-09-01

著者

• Kwak Keun-chang

  Dept. Of Electrical And Computer Eng. University Of Alberta

• Kim Dong‐hwa

  Hanbat National Univ. Daejeon Kor

• KIM Dong-Hwa

  Dept. of Control an Instrumentation Eng., Hanbat National University

• 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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