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
A hybrid neuro-fuzzy PID controller
Fuzzy Sets and Systems
Water bath temperature control with a neural fuzzy inference network
Fuzzy Sets and Systems
A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules
Fuzzy Sets and Systems
Tracking control of a manipulator under uncertainty by FUZZY P+ID controller
Fuzzy Sets and Systems
Stable adaptive control using fuzzy systems and neural networks
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
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This paper proposes an adaptive network architecture, which can emulate the human knowledge as the fuzzy logic rule, and its applications as the controller for nonlinear systems. The structure of this proposed network, multi-input Fuzzy Rule Emulated Network or FREN, is derived based on human knowledge in the form of fuzzy IF-THEN rules. The initial setting of its parameters can be intuitively chosen from expert's experience. During the learning phase based on the gradient search, the learning rate can be adapted itself to remain the stability with the Lyapunov method. The performance of our network is presented by using this network as controller for the single invert pendulum plant and the water bath temperature control system. The comparison results with other conventional control algorithms such as artificial neural networks and PID controllers can be illustrated in each example.