Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Fuzzy Sets and Systems - Special issue on diagnostics and control through neural interpretations of fuzzy sets
A hybrid neuro-fuzzy PID controller
Fuzzy Sets and Systems
Water bath temperature control with a neural fuzzy inference network
Fuzzy Sets and Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Stability analysis of nonlinear multivariable Takagi-Sugeno fuzzy control systems
IEEE Transactions on Fuzzy Systems
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Learning control using fuzzified self-organizing radial basis function network
IEEE Transactions on Fuzzy Systems
Robust backpropagation training algorithm for multilayered neural tracking controller
IEEE Transactions on Neural Networks
Learning and tuning fuzzy logic controllers through reinforcements
IEEE Transactions on Neural Networks
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
International Journal of Intelligent Systems Technologies and Applications
A fuzzy GARCH model applied to stock market scenario using a genetic algorithm
Expert Systems with Applications: An International Journal
A Fuzzy Asymmetric GARCH model applied to stock markets
Information Sciences: an International Journal
Fuzzy Sets and Systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Stability and robustness of fuzzy adaptive control of nonlinear systems
Applied Soft Computing
Engineering Applications of Artificial Intelligence
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In this paper, the adaptive controller inspired by the neuro-fuzzy controller is proposed. Its structure, called fuzzy rules emulated network (FREN), is derived based on the fuzzy if-then rules. This structure not only emulates the fuzzy control rules but also allows the initial value of controller's parameters to be intuitively chosen. These parameters are further adjusted during system operation using a method similar to the steepest descent technique. The learning rate selection criteria based on Lyapunov's stability condition is also presented. FREN controller is applied to control various nonlinear systems, for examples, the single invert pendulum plant, the water bath temperature control, the high voltage direct current transmission system and the robotic system. Computer simulations results indicate that the proposed controller is able to control the target systems satisfactory.