Neural network implementation of fuzzy logic
Fuzzy Sets and Systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
An efficient algorithm for fuzzy weighted average
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Fuzzy weighted averages revisited
Fuzzy Sets and Systems - Information processing
Identification of complex systems based on neural and Takagi-Sugeno fuzzy model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A TSK-type neurofuzzy network approach to system modeling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
Structure identification of generalized adaptive neuro-fuzzy inference systems
IEEE Transactions on Fuzzy Systems
Neural networks that learn from fuzzy if-then rules
IEEE Transactions on Fuzzy Systems
A neural fuzzy system with linguistic teaching signals
IEEE Transactions on Fuzzy Systems
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reinforcement self-organizing interval type-2 fuzzy system with ant colony optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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A fuzzified Takagi-Sugeno-Kang (TSK)-type neural fuzzy inference network (FTNFIN) that is capable of handling both linguistic and numerical information simultaneously is proposed in this paper. FTRNFN solves the disadvantages of most existing neural fuzzy systems which can only handle numerical information. The inputs and outputs of FTNFIN may be fuzzy numbers with any shapes or numerical values. Structurally, FTNFIN is a fuzzy network constructed from a series of fuzzy if-then rules with TSK-type consequent parts. The @a-cut technique is used in input fuzzification and consequent part computation, which enables the network to simultaneously handle both numerical and linguistic information. There are no rules in FTNFIN initially since they are constructed on-line by concurrent structure and parameter learning. FTNFIN is characterized by small network size and high learning accuracy, and can be applied to linguistic information processing. The network has been applied to the learning of fuzzy if-then rules, a mathematical function with fuzzy inputs and outputs, and truck backing control problem. Good simulation results are achieved from all these applications.