Approximation of Multi-Dimensional Chaotic Dynamics by Using Multi-Stage Fuzzy Inference Systems and the GA(Special Section on Nonlinear Theory and its Applications)

スポンサーリンク

概要

• 論文の詳細を見る

• This paper deals with the approximation of multi-dimensional chaotic dynamics by using the multi-stage fuzzy inference system. The number of rules included in multi-stage fuzzy inference systems is remarkably smaller conpared to conventional fuzzy inference systems where the number of rules are proportional to an exponential of the number of input variables. We also propose a method to optimize the shape of mambership function and the appropriate selection of input variables based upon the genetic algorithm (GA). The method is applied to the approximation of typical multi-dimensional chaotic dynamics. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the mambership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is defined by using the prediction error. Experimental results lead us to a relevant selection of the number of input variables and the number of stages by considering the computational cost and the requirement. Besides the GA in the optimization of membership function, we use the Ga to determine the input variables and the number of input. The selection of input variable to each stage, and the number of stages are also discussed. The simulation study for multi-dimensional chaotic dynamics shows that the inference system gives better prediction compared to the prediction by the neural network.

• 社団法人電子情報通信学会の論文
• 2001-09-01

著者

• Tokinaga Shozo

  The Graduate School Of Economics Kyushu University

• KISHIKAWA Yoshinori

  the Graduate School of Economics, Kyushu University

• Kishikawa Y

  The Graduate School Of Economics Kyushu University

関連論文

• Controlling the Chaotic Dynamics by Uusing Approximated System Equations Obtained by the Genetic Programming(Special Section on Nonlinear Theory and its Applications)
• Approximation of Chaotic Dynamics by Using Smaller Number of Data Based upon the Genetic Programming and Its Applications(Special Section on Digital Signal Processing)
• Approximation of Multi-Dimensional Chaotic Dynamics by Using Multi-Stage Fuzzy Inference Systems and the GA(Special Section on Nonlinear Theory and its Applications)
• Approximation of Chaotic Dynamics for Input Pricing at Service Facilities Based on the GP and the Control of Chaos
• Prediction of Future Stock Trends by Using Two-Stage Hierarchical Systems Based on the Segment Categorization and Recognition of Series of Category Symbols Using the Genetic Programming

スポンサーリンク