Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
A neural fuzzy control system with structure and parameter learning
Fuzzy Sets and Systems - Special issue on modern fuzzy control
Robust adaptive control
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
A course in fuzzy systems and control
A course in fuzzy systems and control
A note on universal approximation by hierarchical fuzzy systems
Information Sciences: an International Journal - Special issue analytical theory of fuzzy control with applications
Modeling of hierarchical fuzzy systems
Fuzzy Sets and Systems - Theme: Learning and modeling
On the construction of hierarchical fuzzy systems models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic fuzzy neural networks-a novel approach to functionapproximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic neuro-fuzzy modeling framework with application tomaterial property prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analysis and design of hierarchical fuzzy systems
IEEE Transactions on Fuzzy Systems
On multistage fuzzy neural network modeling
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
A class of hierarchical fuzzy systems with constraints on the fuzzy rules
IEEE Transactions on Fuzzy Systems
Approximation Capabilities of Hierarchical Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Some new results on system identification with dynamic neural networks
IEEE Transactions on Neural Networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
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Hierarchical fuzzy neural networks can use less rules to model nonlinear system with high accuracy. But the normal training method for hierarchical fuzzy neural networks is very complex. In this paper we modify the backpropagation approach and employ a time-varying learning rate that is determined from input-output data and model structure. Stable learning algorithms for the premise and the consequence parts of the fuzzy rules are proposed. The calculation of the learning rate does not need any prior information such as estimation of the modeling error bounds. The new algorithms are very simple, we can train each sub-block of the hierarchical fuzzy neural networks independently.