Multilayer feedforward networks are universal approximators
Neural Networks
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Prediction of chaotic time series based on the recurrent predictor neural network
IEEE Transactions on Signal Processing
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent fuzzy-neural model for dynamic system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Supervisory recurrent fuzzy neural network control of wing rock for slender delta wings
IEEE Transactions on Fuzzy Systems
Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs
IEEE Transactions on Neural Networks
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This paper presents a recurrent fuzzy neural network (RFNN) controller to provide sensorless speed control for the two-phase linear brushless DC motor. It is not easy to arrange current and speed sensors along the linear motor's required railway. The RFNN controller has the capability of tracing the system dynamics due to the inherent learning function. With the help of RFNN controller, the conventional PID control can be made to provide better performance by tracing the system dynamics. The paper will demonstrate dynamic performance improvement scenarios. The recurrent structure also enhances the conventional fuzzy neural network (FNN) controller with embedded memory function. The results from experimental evaluation indicate that the PID with RFNN controller can provide better performance than the conventional PID controller.