Fuzzy Logic-A Modern Perspective
IEEE Transactions on Knowledge and Data Engineering
A fast learning algorithm for parismonious fuzzy neural systems
Fuzzy Sets and Systems - Information processing
An adaptive neuro-fuzzy filter design via periodic fuzzy neural network
Signal Processing - Special section on content-based image and video retrieval
Eliciting transparent fuzzy model using differential evolution
Applied Soft Computing
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
An Online Self-constructing Fuzzy Neural Network with Restrictive Growth
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A fast and compact fuzzy neural network for online extraction of fuzzy rules
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Engineering Applications of Artificial Intelligence
Prediction of time sequence using recurrent compensatory neuro-fuzzy systems
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
A generalized online self-constructing fuzzy neural network
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network
Neural Processing Letters
Fault fuzzy rule extraction from AC motors by neuro-fuzzy models
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Automatica (Journal of IFAC)
Rule base simplification by using a similarity measure of fuzzy sets
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper presents a fuzzy neural network system (FNNS) for implementing fuzzy inference systems. In the FNNS, a fuzzy similarity measure for fuzzy rules is proposed to eliminate redundant fuzzy logical rules, so that the number of rules in the resulting fuzzy inference system will be reduced. Moreover, a fuzzy similarity measure for fuzzy sets that indicates the degree to which two fuzzy sets are equal is applied to combine similar input linguistic term nodes. Thus we obtain a method for reducing the complexity of a fuzzy neural network. We also design a new and efficient on-line initialization method for choosing the initial parameters of the FNNS. A computer simulation is presented to illustrate the performance and applicability of the proposed FNNS. The result indicates that the FNNS still has desirable performance under fewer fuzzy logical rules and adjustable parameters