Digital control system analysis and design (3rd ed.)
Digital control system analysis and design (3rd ed.)
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Temperature control with a neural fuzzy inference network
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
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This paper addresses a Compensatory Wavelet Neuro-Fuzzy System (CWNFS) for temperature control. The proposed CWNFS model is five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). We adopt the non-orthogonal and compactly supported functions as wavelet neural network bases. Besides, the compensatory fuzzy reasoning method is used in adaptive fuzzy operations that can make the fuzzy logic system more adaptive and effective. An on-line learning algorithm, which consists of structure learning and parameter learning, is presented. The structure learning is based on the degree measure to determine the number of fuzzy rules and wavelet functions. The parameter learning is based on the gradient descent method to adjust the shape of membership function, compensatory operations and the connection weights of WNN. Simulation results have been given to illustrate the performance and effectiveness of the proposed model.