Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic synthesis of fuzzy logic controllers in turning
Fuzzy Sets and Systems
Genetic algorithms for learning the rule base of fuzzy logic controller
Fuzzy Sets and Systems
A learning process for fuzzy control rules using genetic algorithms
Fuzzy Sets and Systems
Redundant fuzzy rules exclusion by genetic algorithms
Fuzzy Sets and Systems
A genetic-algorithm-based method for tuning fuzzy logic controllers
Fuzzy Sets and Systems
A GA-based fuzzy adaptive learning control network
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Reduction of fuzzy control rules by means of premise learning - method and case study
Fuzzy Sets and Systems - Fuzzy systems
Genetic fuzzy control for time-varying delayed uncertain systems with a robust stability safeguard
Applied Mathematics and Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
Fuzzy control of pH using genetic algorithms
IEEE Transactions on Fuzzy Systems
Optimal hedge-algebras-based controller: Design and application
Fuzzy Sets and Systems
Development of genetic fuzzy logic controllers for complex production systems
Computers and Industrial Engineering
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Self-learning fuzzy logic controllers for pursuit-evasion differential games
Robotics and Autonomous Systems
Design of self tuning fuzzy controllers for nonlinear systems
Expert Systems with Applications: An International Journal
Hi-index | 0.21 |
Logic rules and membership functions are two key components of a fuzzy logic controller (FLC). If only one component is learned, the other one is often set subjectively thus can reduce the applicability of FLC. If both components are learned simultaneously, a very long chromosome is often needed thus may deteriorate the learning performance. To avoid these shortcomings, this paper employs genetic algorithms to learn both logic rules and membership functions sequentially. We propose a bi-level iterative evolution algorithm in selecting the logic rules and tuning the membership functions for a genetic fuzzy logic controller (GFLC). The upper level is to solve the composition of logic rules using the membership functions tuned by the lower level. The lower level is to determine the shape of membership functions using the logic rules learned from the upper level. We also propose a new encoding method for tuning the membership functions to overcome the problem of too many constraints. Our proposed GFLC model is compared with other similar GFLC, artificial neural network and fuzzy neural network models, which are trained and validated by the same examples with theoretical and field-observed car-following behaviors. The results reveal that our proposed GFLC has outperformed.