New fuzzy wavelet neural networks for system identification and control

It is shown that noise and disturbance in the reference signal are reduced with wavelets and also the variation of somatic gain, the parameter that controls the slope of the activation function in the neural network, leads to more accurate output. Identification results are found to be accurate and speed of their convergence is fast. Next, we simulate a control system for maintaining the output at a desired level by using the identified models. Self-learning FNN controller has been designed in this simulation. Simulation results show that the controller is adaptive and robust.