A Novel Navigation Method for Autonomous Mobile Vehicles
Journal of Intelligent and Robotic Systems
A genetic-designed beta basis function neural network for multi-variable functions approximation
Systems Analysis Modelling Simulation - Special issue: Advances in control and computer engineering
Construction of fuzzy systems using least-squares method and genetic algorithm
Fuzzy Sets and Systems - Theme: Modeling and control
Evolving two-dimensional fuzzy systems
Fuzzy Sets and Systems - Theme: Learning and modeling
Filtering of linear partially observe stochastic systems: the fuzzy logic approach
Dynamics and Control
Contribution to Design of Complex Mechatronic Systems. An Approach through Evolutionary Optimization
Journal of Intelligent and Robotic Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
GA-Based Adaptive Fuzzy-Neural Control for a Class of MIMO Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Design and Implementation of GA-based Fuzzy System on FPGA CHIP
Cybernetics and Systems
Optimum PID controller tuning for AVR system using adaptive tabu search
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Development of genetic fuzzy logic controllers for complex production systems
Computers and Industrial Engineering
Using an efficient immune symbiotic evolution learning for compensatory neuro-fuzzy controller
IEEE Transactions on Fuzzy Systems
Genetic based fuzzy logic controller for a wall-following mobile robot
ACC'09 Proceedings of the 2009 conference on American Control Conference
Engineering Applications of Artificial Intelligence
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Hybrid intelligent systems applied to the pursuit-evasion game
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
ICES'07 Proceedings of the 7th international conference on Evolvable systems: from biology to hardware
A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control
Expert Systems with Applications: An International Journal
Adaptive learning approach of fuzzy logic controller with evolution for pursuit-evasion games
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Tuning PID controller using multiobjective ant colony optimization
Applied Computational Intelligence and Soft Computing
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Differential evolution with local information for neuro-fuzzy systems optimisation
Knowledge-Based Systems
International Journal of Intelligent Information and Database Systems
International Journal of Intelligent Information and Database Systems
Process control using genetic algorithm and ant colony optimization algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance