Self-tuning of fuzzy rules when learning data have a radically changing distribution

2003 ◽  
Vol 144 (4) ◽  
pp. 63-74 ◽  
Author(s):  
Eikou Gonda ◽  
Hitoshi Miyata ◽  
Masaaki Ohkita
Keyword(s):  
Fuzzy Rules ◽  
Self Tuning ◽  
Learning Data

scholarly journals Self-Tuning of Fuzzy Rules with Different Types of MSFs

2004 ◽  
Vol 16 (6) ◽  
pp. 540-550 ◽  
Author(s):  
Eikou GONDA ◽  
Hitoshi MIYATA ◽  
Masaaki OHKITA
Keyword(s):  
Fuzzy Rules ◽  
Different Types ◽  
Self Tuning

Self-Tuning for Fuzzy Rule Generation Based upon Fuzzy Singleton-type Reasoning Method

1999 ◽  
Vol 3 (3) ◽  
pp. 200-206 ◽  
Author(s):  
Yan Shi ◽  
◽  
Masaharu Mizumoto ◽  
Keyword(s):  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Rule Generation ◽  
Nonlinear Functions ◽  
Tuning Parameters ◽  
Tuning Method ◽  
Self Tuning

Using fuzzy singleton-type reasoning method, we propose a self-tuning method for fuzzy rule generation. We give a neurofuzzy learning algorithm for tuning fuzzy rules under fuzzy singleton-type reasoning method, then roughly design initial tuning parameters of fuzzy rules based on a fuzzy clustering algorithm before learning a fuzzy model. This should reduce learning time and fuzzy rules generated by our approach are reasonable and suitable for the identified model. We demonstrate our proposal’s efficiency by identifying nonlinear functions.

scholarly journals Self-tuning of Fuzzy Rules using Piecewise Linear Membership Functions

1996 ◽  
Vol 116 (7) ◽  
pp. 776-784
Author(s):  
Makoto Ohki ◽  
Hitoshi Miyata ◽  
Mikihiro Tanaka ◽  
Masaaki Ohkita
Keyword(s):  
Piecewise Linear ◽  
Fuzzy Rules ◽  
Membership Functions ◽  
Self Tuning

scholarly journals Optimization of DC motor speed control based on fuzzy logic-PID controller

2021 ◽  
pp. 143-153
Author(s):  
Amer Farhan Sheet ◽  
Keyword(s):  
Fuzzy Logic ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Fuzzy Rules ◽  
Motor Speed ◽  
Speed Error ◽  
Self Tuning ◽  
The Difference

In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a change in the speed error (speed error derivative). The output of the FLC, i.e. the parameters of the PID controller, are used to control the speed of the separately excited DC Motor. This study shows that the precisiom feature of the PID controllers and the flexibllity feature of the fuzzy controller are presented in the fuzzy self-tuning PID controller. The fuzzy self – tuning approach implemented on the conventional PID structure improved the dynamic and static response of the system. The salient features of both conventional and fuzzy self-tuning controller outputs are explored by simulation using MATLAB. The simulation results demonstrate that the proposed self-tuned PID controller i.plementd a good dynamic behavior of the DC motor i.e. perfect speed tracking with a settling time, minimum overshoot and minimum steady state errorws.

scholarly journals A Self-Tuning Method of Fuzzy Rules Based

1996 ◽  
Vol 8 (4) ◽  
pp. 757-767 ◽  
Author(s):  
Yan SHI ◽  
Masaharu MIZUMOTO ◽  
Naoyoshi YUBAZAKI ◽  
Masayuki OTANI
Keyword(s):  
Fuzzy Rules ◽  
Tuning Method ◽  
Self Tuning

scholarly journals A Fast Method for Fuzzy Rules Learning with Derivative-Free Optimization by Formulating Independent Evaluations of Each Fuzzy Rule

2021 ◽  
Vol 25 (2) ◽  
pp. 213-225
Author(s):  
Kiyohiko Uehara ◽  
Kaoru Hirota ◽  
◽  
Keyword(s):  
Fuzzy Rule ◽  
Optimization Methods ◽  
Search Space ◽  
Fuzzy Rules ◽  
Fast Method ◽  
Evaluation Functions ◽  
Derivative Free Optimization ◽  
Derivative Free ◽  
Effective Use ◽  
Learning Data

A method is proposed for evaluating fuzzy rules independently of each other in fuzzy rules learning. The proposed method is named α-FUZZI-ES (α-weight-based fuzzy-rule independent evaluations) in this paper. In α-FUZZI-ES, the evaluation value of a fuzzy system is divided out among the fuzzy rules by using the compatibility degrees of the learning data. By the effective use of α-FUZZI-ES, a method for fast fuzzy rules learning is proposed. This is named α-FUZZI-ES learning (α-FUZZI-ES-based fuzzy rules learning) in this paper. α-FUZZI-ES learning is especially effective when evaluation functions are not differentiable and derivative-based optimization methods cannot be applied to fuzzy rules learning. α-FUZZI-ES learning makes it possible to optimize fuzzy rules independently of each other. This property reduces the dimensionality of the search space in finding the optimum fuzzy rules. Thereby, α-FUZZI-ES learning can attain fast convergence in fuzzy rules optimization. Moreover, α-FUZZI-ES learning can be efficiently performed with hardware in parallel to optimize fuzzy rules independently of each other. Numerical results show that α-FUZZI-ES learning is superior to the exemplary conventional scheme in terms of accuracy and convergence speed when the evaluation function is non-differentiable.

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