Based On Wavelet Transform Algorithm For System Identification

The perfect mathematical characteristics of wavelets and wavelets transform are doing more and more favor from control scientists and engineering personnel. In 1991, Astrom--a Sweden'control scientist advanced that wavelets approximation would become the latest researching method in system modeling, identification and control. During the later period over one decade, following with the development of wavelet theory, wavelet has been broadly applied in system identification and control, and partially resolved some problems in system identification and control science. On the base of predecessors achievements and my research work, the new algorithms about the application of wavelets theory in system modeling and control are presented in this paper, and the main contents as following:This paper introduces the classical control theory, the simplest modeling and identification thinking. And application of wavelet function and wavelet transform to study the system. Based on wavelet transform non-parameter identification system is mainly used wavelet or wavelet packet approximation system impulse response process, and used orthogonal transform scale identification data weakened the impact of noise to improve identification accuracy. System Identification for the control of the use of orthogonal wavelet in the identification of data through the noise contained in the scale transformation, the margin of its small and weakened the impact of noise, improve the ratio output signal to noise.In this paper, the inverse pseudo-random binary sequence are introduced, and its multi-resolution analysis of research and to articulate. In this paper, Haar wavelet is chosen as a system identification of the basic wavelet in the non-linear system. Different wavelets for the same system have obtained a haar wavelet as a system identification of the basic wavelet system identification, more accurate. Thereafter, the paper has also given a pseudo-random binary sequence against the wavelet multi-resolution analysis as input signal of measured on the system, the accuracy is higher than that in the simulation system as a system identification by haar wavelet input signals. For its characteristics, this method can greatly improve the input signal characteristics (i.e., a wavelet characteristics has the characteristics of pseudo-random binary sequence) and accurate identification parameters.