Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A proposal for a defuzzification strategy by the concept of sensitivity analysis
Fuzzy Sets and Systems
Handling the nonlinearity of a fuzzy logic controller at the transition between rules
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Cooperative neighbors in defuzzification
Fuzzy Sets and Systems
A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering
Neural Processing Letters
The influence of learning on evolution
Adaptive individuals in evolving populations
An accurate and cost-effective COG defuzzifier without the multiplier and the divider
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Study on the Evolutionary Adaptive Defuzzification Methods in Fuzzy Modeling
International Journal of Hybrid Intelligent Systems
Improved genetic algorithm for multidisciplinary optimization of composite laminates
Computers and Structures
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This paper proposes a design technique of optimal center of gravity (COG) defuzzifier using the Lamarckian coadaptation of learning and evolution. The proposed COG defuzzifier is specified by various design parameters such as the centers, widths, and modifiers of MFs. The design parameters are adjusted with the Lamarckian co-adaptation of learning and evolution, where the learning performs a local search of design parameters in an individual COG defuzzifier, but the evolution performs a global search of design parameters among a population of various COG defuzzifiers. This co-adaptation scheme allows to evolve much faster than the non-learning case and gives a higher possibility of finding an optimal solution due to its wider searching capability. An application to the truck backer-upper control problem of the proposed co-adaptive design method of COG defuzzifier is presented. The approximation ability and control performance are compared with those of the conventionally simplified COG defuzzifier in terms of the fuzzy logic controller's approximation error and the average tracing distance, respectively.