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This paper aims at the Genetic Algorithm (GA's) based tuning of fuzzy logic controller (FLC). A two-step approach is proposed to tune a fuzzy logic controller using genetic algorithm. Moreover, it has been tried to develop a stepwise method to tune a fuzzy logic controller with GA in less number of generations. Special attention has been given to the learning of knowledge base which can be used for...
This paper proposes a new approach based on Quantum Evolutionary Algorithm (QEA) for effective selection and definition of fuzzy if-then rules to design Fuzzy Logic Controllers (FLCs). The majority of works done on designing FLCs were based on knowledgebase derived from imprecise heuristic knowledge of experienced operators or persons but they were difficult and time consuming to evaluate. The proposed...
Flexible manipulator systems exhibit many advantages over their traditional (rigid) counterparts. However, they have not been favored in production industries due to its obvious disadvantages in controlling the manipulator. This paper presents theoretical investigation into the dynamic modeling and characterization of a constrained two-link flexible manipulator, by using finite element method. The...
Fuzzy Logic Controllers (FLCS) are rule-based system that successfully incorporate the flexibility of human-decision making by means of the use of fuzzy set theory. This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. A three-stage evolution framework that uses Genetic Programming (GP) and Genetic Algorithms (GAS) evolves...
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