Accession Number : ADA299677
Title : Parameter Estimation in Chaotic Systems.
Descriptive Note : Technical rept.,
Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
Personal Author(s) : Hung, Elmer S.
PDF Url : ADA299677
Report Date : APR 1995
Pagination or Media Count : 187
Abstract : This report examines how to estimate the parameters of a chaotic system given noisy observations of the State behavior of the system. Investigating parameter estimation for chaotic systems is interesting because of possible applications for high precision measurement and for use in other signal processing, communication, and control applications involving chaotic systems. In this report, we examine theoretical issues regarding parameter estimation in chaotic systems and develop an efficient algorithm to perform parameter estimation. We discover two properties that are helpful for performing parameter estimation on non-structurally stable systems. First, it turns out that most data in a time series of state observations contribute very little information about the underlying parameters of a system, while a few sections of data may be extraordinarily sensitive to parameter changes. Second, for one-parameter families of systems, we demonstrate that there is often a preferred direction in parameter space governing how easily trajectories of one system can shadow" trajectories of nearby systems. This asymmetry of shadowing behavior in parameter space is proved for certain families of maps of the interval. Numerical evidence indicates that similar results may be true for a wide variety of other systems. Using the two properties cited above, we devise an algorithm for performing parameter estimation. Standard parameter estimation techniques such as the extended Kalman filter perform poorly on chaotic systems because of divergence problems. The proposed algorithm achieves accuracies several orders of magnitude better than the Kalman filter and has good convergence properties for large data sets. In some systems the algorithm converges at a rate proportional to $
Descriptors : *PARAMETERS, *CHAOS, *ESTIMATES, ALGORITHMS, SIGNAL PROCESSING, MEASUREMENT, NUMERICAL ANALYSIS, TIME SERIES ANALYSIS, KALMAN FILTERING, ACCURACY, EFFICIENCY, SENSITIVITY, PRECISION, SAMPLING, CONVERGENCE, ARTIFICIAL INTELLIGENCE, MAPS, BEHAVIOR, CONTROL THEORY, TRAJECTORIES, INTERVALS.
Subject Categories : Cybernetics
Distribution Statement : APPROVED FOR PUBLIC RELEASE