Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Soft computing for control of non-linear dynamical systems
Soft computing for control of non-linear dynamical systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms: Concepts and Designs with Disk
Genetic Algorithms: Concepts and Designs with Disk
Soft Computing and Fractal Theory for Intelligent Manufacturing
Soft Computing and Fractal Theory for Intelligent Manufacturing
A framework for analysis and synthesis of fuzzy linguistic control systems
A framework for analysis and synthesis of fuzzy linguistic control systems
A direct adaptive neural control scheme with integral terms: Research Articles
International Journal of Intelligent Systems - Soft Computing for Modeling, Simulation, and Control of Nonlinear Dynamical Systems
Toward a framework for the specification of hybrid fuzzy modeling: Research Articles
International Journal of Intelligent Systems - Soft Computing for Modeling, Simulation, and Control of Nonlinear Dynamical Systems
Fuzzy system parameters discovery by bacterial evolutionary algorithm
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Metaheuristic algorithms for inverse problems
International Journal of Innovative Computing and Applications
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We describe in this paper the evolutionary design of hybrid intelligent systems using Hierarchical Genetic Algorithms (HGAs). The evolutionary approach can be used for fuzzy system optimisation in intelligent control. In particular, we consider the problem of optimising the number of rules and membership functions using an evolutionary approach. The HGA enables the optimisation of the fuzzy system design for a particular application. We illustrate the approach with two cases of intelligent control. Simulation results for both applications show that we are able to find an optimal set of rules and membership functions for the fuzzy control system. We also describe the application of the evolutionary approach for the problem of designing hybrid intelligent systems in time series prediction. In this case, the goal is to design the best predictor for complex time series. Simulation results show that the evolutionary approach optimises the hybrid intelligent systems in time series prediction.