Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Predicting a chaotic time series using a fuzzy neural network
Information Sciences: an International Journal
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Rule-based modeling: fast construction and optimal manipulation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
NFI: a neuro-fuzzy inference method for transductive reasoning
IEEE Transactions on Fuzzy Systems
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
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In this study, we introduce and investigate a genetically optimized fuzzy relation-based polynomial neural networks with the aid of information granulation (IG_gFRPNN), develop a comprehensive design methodology involving mechanisms of genetic optimization with symbolic gene type. With the aid of the information granules based on C-Means clustering, we can determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of IG_gFRPNN leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. The proposed model is contrasted with the performance of the conventional intelligent models shown in the literatures.