Design of robust fuzzy neural network controller with reduced rule base
International Journal of Hybrid Intelligent Systems
A Novel Optimization Strategy for the Nonlinear Systems Identification
Computational Intelligence and Security
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
Fuzzy Neural Network-Based Sliding Mode Control for Non-spinning Warhead with Moving Mass Actuators
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Non-stationary power signal processing for pattern recognition using HS-transform
Applied Soft Computing
Intelligent Traffic Control Decision Support System
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
A Study on Lateral Control of Autonomous Vehicles via Fired Fuzzy Rules Chromosome Encoding Scheme
Journal of Intelligent and Robotic Systems
IEEE Transactions on Neural Networks
A new iterative learning controller using variable structure Fourier neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Sciences: an International Journal
Information Sciences: an International Journal
Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm
Expert Systems with Applications: An International Journal
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In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.