Genetic learning of fuzzy reactive controllers

Genetic learning of fuzzy reactive controllers

Genetic Fuzzy Logic Controllers

The conventional fuzzy logic controller (CFLC) is limited in application, because its logic rules and membership functions have to be preset with expert knowledge. To avoid such drawbacks, a genetic fuzzy logic controller (GFLC) is proposed by employing an iterative evolution algorithm to promote the learning performance of logic rules and the tuning effectiveness of membership functions from e...

Genetic Optimization of Fuzzy Logic controllers

The VHDL-AMS based genetic optimization of fuzzy logic controller for movement control systems is discussed here. The designs have been carried out in the digital domain with HDL. The basic components of the fuzzy logic controller are designed using VHDL-AMS. The proposed work focuses on control of speed with respect to input parameter such as Alignment & distance with triangular membership fun...

Learning Fuzzy Reactive Behaviors

This paper is concerned with the learning of basic behaviors in autonomous robots. In this way, we present a method for the adaptation of basic reactive behaviors implemented as fuzzy controllers applying a genetic algorithm to the evolution of the fuzzy rule system. In this sense, we show our experiments in the evolution of control rules based on symbolic concepts represented as linguistic lab...

Designing Fuzzy Net Controllers using Genetic Algorithms

As control system tasks become more demanding, more robust controller design methodologies are needed. A Genetic Algorithm (GA) optimizer, which utilizes natural evolution strategies, o ers a promising technology that supports optimization of the parameters of fuzzy logic and other parameterized non-linear controllers. This paper shows how GAs can e ectively and e ciently optimize the performan...