Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Particle Filtering Approach to Change Detection for Nonlinear Systems | EURASIP Journal on Applied Signal Processing 2004 15 2295-2305 2004 Hindawi Publishing Corporation A Particle Filtering Approach to Change Detection for Nonlinear Systems Babak Azimi-Sadjadi Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute Troy NY 12180-3590 USA Email babak@ The Institute for Systems Research University of Maryland College Park MD 20742 USA P. S. Krishnaprasad The Institute for Systems Research University of Maryland College Park MD 20742 USA Email krishna@ Received 13 September 2003 Revised 22 March 2004 We present a change detection method for nonlinear stochastic systems based on particle filtering. We assume that the parameters of the system before and after change are known. The statistic for this method is chosen in such a way that it can be calculated recursively while the computational complexity of the method remains constant with respect to time. We present simulation results that show the advantages of this method compared to linearization techniques. Keywords and phrases nonlinear filtering generalized likelihood ratio test CUSUM algorithm online change detection. 1. INTRODUCTION Page states the change detection problem as follows 1 Whenever observations are taken in order it can happen that the whole set of observations can be divided into subsets each of which can be regarded as a random sample from a common distribution each subset corresponding to a different parameter value of the distribution. The problems to be considered in this paper are concerned with the identification of the subsamples and the detection of changes in the parameter value We refer to a change or an abrupt change as any change in the parameters of the system that happens either instantaneously or much faster than any change that the nominal bandwidth of the system allows. The key difficulty of all change detection methods is that of detecting intrinsic changes that are not necessarily directly .
