A New Transformed Input-Domain ANFIS for Highly Nonlinear System Modeling and Prediction

スポンサーリンク

概要

• 論文の詳細を見る

• In two-or more-dimensional systems where the components of the sample data are strongly correlated, it is not proper to divide the input space into several subspaces without considering the correlation. In this paper, we propose the us-age of the method of principal component in order to uncorrelate and remove any redundancy from the input space of the adaptive neuro-fuzzy inference system (ANFIS). This leads to an effective partition of the input space to the fuzzy model and significantly reduces the modeling error. A computer simulation for two frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS under the same conditions.

• 社団法人電子情報通信学会の論文
• 2001-08-01

著者

• YAHAGI Takashi

  The authors are with the Graduate School of Science and Technology, Chiba University

• Abdelrahim E

  The Authors Are With The Graduate School Of Science And Technology Chiba University

• ABDELRAHIM Elsaid

  The authors are with the Graduate School of Science and Technology, Chiba University

• Yahagi Takashi

  The Authors Are With The Graduate School Of Science And Technology Chiba University

関連論文

• A Stop Criterion for Turbo Code to Reduce Decoding Iterations
• Detection of Nonlinearly Distorted M-ary QAM Signals Using Self-Organizing Map
• Multi-Input Single-Output Nonlinear Adaptive Digital Filters Using Recurrent Neural Networks
• A Method of Model Reference Adaptive Control for MIMO Nonlinear Systems Using Neural Networks
• A New Transformed Input-Domain ANFIS for Highly Nonlinear System Modeling and Prediction
• Image Compression by New Sub-Image Block Classification Techniques Using Neural Networks

スポンサーリンク