The Design of Multi-Stage Fuzzy Inference Systems with Smaller Number of Rules Based upon the Optimization of Rules by Using 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 shows the design of multi-stage fuzzy inference system with smaller number of rules based upon the optimization of rules by using the genetic algorithm. Since the number of rules of fuzzy inference system increases exponentially in proportion to the number of input variables powered by the number of membership function, it is preferred to divide the inference system into several stages (multi-stage fuzzy inference system) and decrease the number of rules compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and the output of the stage is treated as an input to the next stage. If we use the simplified inference scheme and assume the shape of membership function is given, the same backpropagation algorithm is available to optimize the weight of each rule as is usually used in the single stage inference system. On the other hand, the shape of the membership function is optimized by using the GA (genetic algorithm) where the characteristics of the membership function is represented as a set of string to which the crossover and mutation operation is applied. By combining the backpropagation algorithm and the GA, we have a comprehensive optimization scheme of learning for the multi-stage fuzzy inference system. The inference system is applied to the automatic bond rating based upon the financial ratios obtained from the financial statement by using the prescribed evaluation of rating published by the rating institution. As a result, we have similar performance of the multi-stage fuzzy inference system as the single stage system with remarkably smaller number of rules.

• 社団法人電子情報通信学会の論文
• 1999-09-25

著者

• Tan Kangrong

  Faculty Of Economics Kurume University

• Tokinaga Shozo

  Faculty Of Economics Kyushu University

• Tan Kangrong

  Faculty Of Economics Kyushu University

関連論文

• Identification of stochastic process based on genetic algorithm (人工知能と知識処理)
• Identification of stochastic process based on Genetic Algorithm (知能と複雑系)
• The Design of Multi-Stage Fuzzy Inference Systems with Smaller Number of Rules Based upon the Optimization of Rules by Using the GA (Special Section on Nonlinear Theory and Its Applications)
• Modeling Returns Distribution Based upon Radical Normal Distributions
• Approximating Probability Distribution Function based upon Mixture Distribution Optimized by Genetic Algorithm and its Application to Tail Distribution Analysis using Importance Sampling Method
• Modeling and Analysis of Agent-based Artificial Demand-Supply Market by Using the Genetic Programming and its Applications
• Forecasting the Stock Option Prices by Using the Fractal Geometry and its Application
• Improved Estimation of Tail Distribution of Link Delays by Using Networks Tomography Based on the Genetic Programming and the Importance Sampling

スポンサーリンク