A novel approach to neuro-fuzzy classification
Neural Networks
A Transductive Neuro-Fuzzy Force Control: An Ethernet-Based Application to a Drilling Process
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Implicitly-supervised learning and its application to fuzzy pattern classifiers
Information Sciences: an International Journal
Fuzzy Kohonen clustering networks for interval data
Neurocomputing
Regularization through fuzzy discrete SVM with applications to customer ranking
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Fuzzy classifier based on fuzzy support vector machine
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The perceptron algorithm, one of the class of gradient descent techniques, has been widely used in pattern recognition to determine linear decision boundaries. While this algorithm is guaranteed to converge to a separating hyperplane if the data are linearly separable, it exhibits erratic behavior if the data are not linearly separable. Fuzzy set theory is introduced into the perceptron algorithm to produce a ``fuzzy algorithm'' which ameliorates the convergence problem in the nonseparable case. It is shown that the fuzzy perceptron, like its crisp counterpart, converges in the separable case. A method of generating membership functions is developed, and experimental results comparing the crisp to the fuzzy perceptron are presented.