Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Fuzzy engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Evolving the Topology and the Weights of Neural Networks Using a Dual Representation
Applied Intelligence
Subsethood based adaptive linguistic networks for pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling
IEEE Transactions on Fuzzy Systems
Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Distributed fuzzy learning using the MULTISOFT machine
IEEE Transactions on Neural Networks
Fuzzy multi-layer perceptron, inferencing and rule generation
IEEE Transactions on Neural Networks
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This paper employs a simple genetic algorithm (GA) to search for an optimal set of parameters for a novel subsethood product fuzzy neural network introduced elsewhere, and to demonstrate the pattern classification capabilities of the network. The search problem has been formulated as an optimization problem with an objective to maximize the number of correctly classified patterns. The performance of the network, with GA evolved parameters, is evaluated by computer simulations on Ripley's synthetic two class data. The network performed excellently by being at par with the Bayes optimal classifier, giving the best possible error rate of 8%. The evolutionary subsethood product network outperformed all other models with just two rules.