Control and identification of non-linear systems affected by noise using wavelet network

摘要: The present work demonstrates an application to approximate, control and denoise a continuous non-linear signal, using wavelet coefficients neural network. Adaptive Least mean square (∞-LMS)algorithm is used for the parameter adjustment, are making system fast denoising it. Neural networks have been established as general approximation tool fitting nonlinear models from input/output data. On other hand, recently introduced decomposition emerges new powerful approximation. procedure based adaptive remains same network only concept of compression reference signal adopted. In control, identification plant done offline adjustments controller parameters performed on-line. effectiveness proposed architecture applied Identification unknown systems discussed extensive simulation results presented.