Partitioning of Linearly Transformed Input Space in Adaptive Network Based Fuzzy Inference System

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概要

• 論文の詳細を見る

• This paper presents a new effective partitioning technique of linearly transformed input space in Adaptive Network based Fuzzy Inference System (ANFIS). The ANFIS is the fuzzy system with a hybrid parameter learning nethod, which is composed of a gradient and a least square method. The input space can be partitioned flexibly using new modeling inputs, which are the weighted linear comvination of the original inputs by the proposed input partitioning technique, thus, the parameter learning time and the modeling error of ANFIS can be reduced. The simulation result illustrates the effectiveness of the proposed technique.

• 社団法人電子情報通信学会の論文
• 2001-01-01

著者

• Won Sangchul

  The Suthors Are With The Department Of Electronic And Electrical Engineering Pohang University Of Sc

• RYU Jeyoung

  The suthors are with the Department of Electronic and Electrical Engineering, Pohang University of S

• Ryu Jeyoung

  The Suthors Are With The Department Of Electronic And Electrical Engineering Pohang University Of Sc

関連論文

• Partitioning of Linearly Transformed Input Space in Adaptive Network Based Fuzzy Inference System

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