Fuzzy Neural Network Theory and Application
Fuzzy Neural Network Theory and Application
Performance measurement of supply chain management: A balanced scorecard approach
Computers and Industrial Engineering
A Fuzzy Neural Network Based on Back-Propagation
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks
Information Sciences: an International Journal
A bacterial evolutionary algorithm for automatic data clustering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Fuzzy system parameters discovery by bacterial evolutionary algorithm
IEEE Transactions on Fuzzy Systems
A fuzzy neural network and its application to pattern recognition
IEEE Transactions on Fuzzy Systems
Artificial Life and Robotics
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This paper presents a novel calculation of fuzzy exponent in the sigmoid functions for fuzzy neural networks. The investigated fuzzy neural network applies fuzzy input signals and crisp connection weights in the network's hidden and output layers. The applied calculation of fuzzy exponent is based on a parametric representation of the fuzzy exponent that is able to provide a crisp output instead of the extension principle's fuzzy output and requires significantly less computational effort than the learning based on @a-cuts. For the training of the network the bacterial memetic algorithm is applied which effectively combines the bacterial evolutionary algorithm with gradient based learning. The method is tested on a benchmark problem and on two real datasets. Comparison to the classical technique concerning the learning time is also provided in the paper.