Structure identification of fuzzy model
Fuzzy Sets and Systems
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Learning Vector Quantization with Training Data Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Evolutionary Computation
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling
IEEE Transactions on Fuzzy Systems
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Development of a systematic methodology of fuzzy logic modeling
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems
IEEE Transactions on Neural Networks
GenSoFNN: a generic self-organizing fuzzy neural network
IEEE Transactions on Neural Networks
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This paper shows fundamentals and applications of the novel parametric fuzzy cerebellar model articulation controller (P-FCMAC) network. It resembles a neural structure that derived from the Albus CMAC algorithm and Takagi-Sugeno-Kang parametric fuzzy inference systems. The Gaussian basis function is used to model the hypercube structure and the linear parametric equation of the network input variance is used to model the TSK-type output. A self-constructing learning algorithm, which consists of the self-clustering method (SCM) and the backpropagation algorithm, is proposed. The proposed the SCM scheme is a fast, one-pass algorithm for a dynamic estimation of the number of hypercube cells in an input data space. The clustering technique does not require prior knowledge of things such as the number of clusters present in a data set. The backpropagation algorithm is used to tune the adjustable parameters. Illustrative examples were conducted to show the performance and applicability of the proposed model.