Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Neuro-fuzzy architectures and hybrid learning
Neuro-fuzzy architectures and hybrid learning
An interval-valued fuzzy inference method: some basic properties
Fuzzy Sets and Systems
IEEE Transactions on Fuzzy Systems
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
Generation of interval-valued fuzzy implications from Kα operators
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
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The system composed of various implication-based neurofuzzy networks in one parallel structure is proposed in this paper. Different phases of data processing are distinguished, i.e. learning, testing, and problem solving. A competetive learning of the neuro-fuzzy networks is employed. This learning method refers to the first layer, which is the same in every network. The system with fuzzy parameters of membership functions is also considered. In this case, the neuro-fuzzy architectures may be viewed as fuzzy inference neural networks with fuzzy parameters, and treated analogously to fuzzy neural networks.