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

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

On Processing of Fuzzy Production Systems

1991 ◽  
Vol 14 (3) ◽  
pp. 301-312
Author(s):  
Stefka P. Stoeva
Keyword(s):  
Time Complexity ◽  
Production Systems ◽  
Modus Ponens ◽  
Knowledge Bases ◽  
Fuzzy Rules ◽  
Software Implementation ◽  
Different Types ◽  
Weighting Coefficients

Knowledge bases in the form of fuzzy production systems with weighting coefficients, i.e. sets of weighted fuzzy rules, are studied. A software implementation of a rule-firing algorithm. based on Zadeh’s generalized modus ponens. is presented. The time complexity of the algorithm proposed is determined. Different types of parallelism in processing of fuzzy production systems are explored.

scholarly journals Stochastic Memristive Interface for Neural Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5587
Author(s):  
Svetlana A. Gerasimova ◽  
Alexey I. Belov ◽  
Dmitry S. Korolev ◽  
Davud V. Guseinov ◽  
Albina V. Lebedeva ◽  
...  
Keyword(s):  
Complex Dynamics ◽  
Synaptic Connection ◽  
Adaptive Modulation ◽  
Postsynaptic Neuron ◽  
Memristive Device ◽  
Software Complex ◽  
Neural Signal Processing ◽  
Different Types ◽  
Self Tuning ◽  
Electronic Neuron

We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO2(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.

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
Sign in / Sign up

Export Citation Format

Share Document