A reinforcement learning-based architecture for fuzzy logic control
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
An introduction to fuzzy control
An introduction to fuzzy control
On the equivalence of neural nets and fuzzy expert systems
Fuzzy Sets and Systems
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
On the principles of fuzzy neural networks
Fuzzy Sets and Systems
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Fuzzy modelling environment for designing fuzzy controllers
Fuzzy Sets and Systems - Special issue on modern fuzzy control
Information Sciences—Informatics and Computer Science: An International Journal
Industrial Applications of Fuzzy Control
Industrial Applications of Fuzzy Control
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fuzzy Control
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Readings in Fuzzy Sets for Intelligent Systems
Readings in Fuzzy Sets for Intelligent Systems
Conditions of output stabilization for nonlinear models in the Takagi--Sugeno's form
Fuzzy Sets and Systems
Engineering Applications of Artificial Intelligence
Stability Analysis and Nonlinear Observer Design using Takagi-Sugeno Fuzzy Models
Stability Analysis and Nonlinear Observer Design using Takagi-Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
A multiple Lyapunov function approach to stabilization of fuzzy control systems
IEEE Transactions on Fuzzy Systems
Neural networks that learn from fuzzy if-then rules
IEEE Transactions on Fuzzy Systems
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
The Stone-Weierstrass theorem and its application to neural networks
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
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In nature, most systems show nonlinear complex behaviors. Among other characteristics, plants present a high degree of oscillation over time. Adaptive algorithms used to approximate such difficult behaviors show some important deficiencies. Many adaptive non-parametric methods cannot reconstruct the trajectories of such complex dynamics. Differential neural networks (DNNs) are no exception. When just one DNN is applied to achieve an approximation, the identification error may significantly differ from zero. A natural trick to overcome this difficulty is to increase the number of neurons or to increase the number of layers. Another possible suggestion is to define a set of neural networks working together (usually in parallel). The members of such a set each work on well-defined trajectories contained in specific subspaces in which the uncertain system may evolve. Nevertheless, a decision system is required to define the contribution of each DNN in the final identification scheme. One of the most successful methodologies for constructing this selector is based on a Takagi-Sugeno (TS) inference system. This paper discusses how to combine the identification properties offered by a continuous neural network and the characteristic decision capabilities of fuzzy methods. The selection of which neural network is activated depends on the decision achieved by a TS fuzzy system. The convergence of this algorithm is proved using a quadratic Lyapunov function. A complete description of the learning laws used for the set of DNN identifiers is also obtained. The Chen circuit and the Rabinovich-Fabrikant system are used to demonstrate the superior performance achieved by this mixed DNN and fuzzy system, usually called a neuro-fuzzy system.