A course in fuzzy systems and control
A course in fuzzy systems and control
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Hierarchical genetic fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Fuzzy Logic, Identification and Predictive Control (Advances in Industrial Control)
Fuzzy Logic, Identification and Predictive Control (Advances in Industrial Control)
Hybrid fuzzy logic control with genetic optimisation for a single-link flexible manipulator
Engineering Applications of Artificial Intelligence
A neuro-coevolutionary genetic fuzzy system to design soft sensors
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Optimal fuzzy logic control for MDOF structural systems using evolutionary algorithms
Engineering Applications of Artificial Intelligence
Development of genetic fuzzy logic controllers for complex production systems
Computers and Industrial Engineering
Genetic fuzzy self-tuning PID controllers for antilock braking systems
Engineering Applications of Artificial Intelligence
An architecture for adaptive fuzzy control in industrial environments
Computers in Industry
A new approach to adaptive fuzzy control: the controller output error method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The paper proposes a new method to automatically extract all fuzzy parameters of a Fuzzy Logic Controller (FLC) in order to control nonlinear industrial processes. The main objective of this paper is the extraction of a FLC from data extracted from a given process while it is being manually controlled. The learning of the FLC is performed by a hierarchical genetic algorithm (HGA), from a set of process-controlled input/output data. The algorithm is composed by a five level structure, being the first level responsible for the selection of an adequate set of input variables. The second level considers the encoding of the membership functions. The individual rules are defined on the third level. The set of rules are obtained on the fourth level, and finally, the fifth level selects the elements of the previous levels, as well as, the t-norm operator, inference engine and defuzzifier methods which constitute the FLC. To optimize the proposed method, the HGA's initial populations are obtained by an initialization algorithm. This algorithm has the main goal of providing a good initial solution for membership functions and rule based populations, enhancing the GA's tuning. Moreover, the HGA is applied to control the dissolved oxygen in an activated sludge reactor within a wastewater treatment plant. The results are presented, showing that the proposed method extracted all the parameters of the fuzzy controller, successfully controlling a nonlinear plant.