Towards increasing the learning speed of gradient descent method in fuzzy system
Fuzzy Sets and Systems
A course in fuzzy systems and control
A course in fuzzy systems and control
Genetic algorithms for learning the rule base of fuzzy logic controller
Fuzzy Sets and Systems
A PI-type controller with self-tuning scaling factors
Fuzzy Sets and Systems
Tuning of fuzzy controller for an open-loop unstable system: a genetic approach
Fuzzy Sets and Systems
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Soft Computing
Genetic Algorithms and Soft Computing
Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives
Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Fuzzy neural network structure identification based on soft competitive learning
International Journal of Hybrid Intelligent Systems
| Hi-index | 0.00 |
The design and implementation of an on-line fuzzy identification method using genetic algorithms (GAs) is reported in this paper. In the proposed algorithm, the rule-table of a fuzzy system is first divided into several independent and much smaller fuzzy systems, which in turn are encoded into separate bit strings for genetic operations. A novel GA updating architecture is then proposed to search the optimal rule-base of these fuzzy systems at each sample interval. The performance of this identification algorithm is evaluated by simulation of a non-linear system. Moreover, an experiment on simple behavior learning of a mobile robot is also reported. The results indicate an improvement in design cycle and convergence to the optimal rule-base within a relatively short period of time.