Evolutionary Subsethood Product Fuzzy Neural Network
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Evolutionary learning of fuzzy neural network using a modified genetic algorithm
Design and application of hybrid intelligent systems
Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm
Fuzzy Sets and Systems
A self-generating fuzzy system with ant and particle swarm cooperative optimization
Expert Systems with Applications: An International Journal
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Multi groups cooperation based symbiotic evolution for TSK-type neuro-fuzzy systems design
Expert Systems with Applications: An International Journal
International Journal of Bio-Inspired Computation
Structural and Multidisciplinary Optimization
An information theoretic approach to generating fuzzy hypercubes for if-then classifiers
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A hybrid evolutionary learning algorithm for TSK-type fuzzy model design
Mathematical and Computer Modelling: An International Journal
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
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A simple, easy to implement alternative method for designing fuzzy logic controllers (FLCs) with symmetrically distributed fuzzy sets in a universe of discourse is introduced. The design parameters include the parameters of the membership functions of the inputs and outputs and the rule base. The method is based on a network implementation of the FLC with real and binary weights with constraints. Due to the presence of the binary weights the backpropagation technique cannot be used. The learning problem is cast as a mixed integer constrained dynamic optimization problem and solved using the genetic algorithm (GA). The crossover and mutation are slightly disrupted in order to cope with the constraints on the binary weights. Training of the controller is carried out in a closed-loop simulation with the controller in the loop