Complex Industrial Systems Modeling Based On Wavelet Networks And Robust Estimation

System modeling is all long regarded as a critical step in automation and optimization. odeling First?is also pursued all the times in industrial applications. Along with technology development at very fast speed and production scale-up, a lot of systems get more and more complicated. Especially increasing hot industrial competition all over the world and people deeply understanding, changing and harmonizing of nature, which make system modeling encounter an unprecedented challenge, task is arduous and urgent to deal with. Under such circumstance, requirements presented by complex industrial process can be far from being satisfied merely with classical control theory, we should draw up-to-date fruits from pertinent subjects such as math, computer science, signal processing technology and so on to develop conceptive theory, technology and method. Combining with the characteristics of the complex industrial process, this thesis aims at studying and discussing complex system modeling, based on wavelet networks and robust estimation theory, and considering practical engineering. The main contributions of the thesis are as follows: [11 The understandings of complex systems are systematically set forth; the basic contents of robust estimation theory are introduced; the applications of wavelet analysis especially wavelet networks in the industrial process are analyzed; the significance of the thesis抯 research is pointed out. [21 The basic principle and method are analyzed of wavelet transformation to eliminate white noise disturbance; then a kind of wavelet transform which can resist the disturbance with contaminated distribution is presented, in which the method to select wavelet coefficients is discussed and some problem of online application are analyzed. This research is possible to lead to a sort of practical information purified technology. [31 Considering that the normal filtering and identification method is not ~1 Iv optimal under non-gauss hypothesis, regress problem based on M estimation is studied; then a robust adaptive FIR filter and robust Kalman filter are studied. Finally a robust adaptive M estimation method combing with wavelet for identifying nonlinear system is proposed, which is optimal under maximum likelihood. [41 Different wavelet networks are analyzed and generalized, and a common multiscale learning paradigm is presented. Using the modified Gram-Schmit algorithm combining with AIC to obtain a parsimonious networks structure and initial parameters, a hierarchical structure design method combing with GA for wavelet network is presented. Then two interior parameters of wavelet networks, the dilation and translation parameters will be optimized with GA combined hierarchical optimization algorithm in order to get more accuracy approximation without adding any wavelet. The results can provide a universal guidance for various wavelet networks. [51 Based on wavelet multiresolution analysis theory, a weighted band-wise method for multirate sampled-data systems identification is proposed. First the problem of multirate sampled-data reconstruction is studied and data reconstruction method based on wavelet network is proposed. Then...