Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
A recurrent fuzzy-neural model for dynamic system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
Structure identification in complete rule-based fuzzy systems
IEEE Transactions on Fuzzy Systems
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
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A self-constructing compensatory neural fuzzy system (SCCNFS) for nonlinear system identification and control is proposed in this paper. The compensatory fuzzy reasoning method uses adaptive fuzzy operations of a neural fuzzy network to make the fuzzy logic system more adaptive and effective. An online learning algorithm is proposed to automatically construct the SCCNFS. The fuzzy rules are created and adapted as online learning proceeds through simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on the backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and that the fuzzy rules that are obtained are more precise. The performance of SCCNFS compares excellently with other various existing models.