基于非線性混沌時(shí)序的系統(tǒng)重構(gòu)、預(yù)測(cè)技術(shù)及其應(yīng)用-資料下載頁(yè)
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【正文】 4 , , . Reliable detection of nonlinearity in experimental timental timeseries with strong periodic ponents. 1998,112: 36138025 Alexei Potapov,Jurgen integral as a tool for distinguishing between dynamics and statistics in time series data. ,120: 36938526 N. H. Packard, J. P. Crutchfield, J. D. Farmer, and R. S. Shaw, Geometry from a time Rev Lett1980,45(9): 712 27 F. Takens. In Dynamical Systems of Turbulence, edited by D. A. Rard and L. S. Young (springer, Berlin 1981)28 . in astronomical time series chaotic and random processes with linear filters. ,359:46948229 , Mane. Detecting strange attractors in fluid turbulence,in: Dynamical systems and Turbulence. of Lecture Notes in Mathematics, springer, Berlin 1986, 30 , , , , A. Politi,and . Dimension increase in filtered chaotic signals. ,60:97998431 qualitatire dynamics from experimental data. 1987,20: 21723632 盛昭瀚 馬軍海. 管理科學(xué)：面對(duì)復(fù)雜性—,98,1(1): 3142 The Reconstruction and Prediction Technology and Application of Nonlinear Chaotic TimeseriesMa Junhai(Management School, Tianjin University, 300072 Tianjin)Zhaohan Sheng (Institute of Systems Engineering, Southeast University, Nanjing 210096)Abstract How to recognize the two difficulties existing in systems with plex structure? One is that system itself is plicated .Other is that in general we gather the chaotic time series of some states of systems only by some “observors” .Hence ,we need techniques which can be used to “recover” the global behavior of systems by the one dimensional projection of the global behavior of systems. This paper will introduce the recent work of the authors in realizing these key techniques.Keywords Nonlinear Chaotic timeseries Fractal,space reconstruction Chaotic model Parameter identification Prediction techonogly
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