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Dynamic Systems Models - New Methods of Parameter and State Estimation (Paperback, Softcover reprint of the original 1st ed. 2016)
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Dynamic Systems Models - New Methods of Parameter and State Estimation (Paperback, Softcover reprint of the original 1st ed. 2016)
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This monograph is an exposition of a novel method for solving
inverse problems, a method of parameter estimation for time series
data collected from simulations of real experiments. These time
series might be generated by measuring the dynamics of aircraft in
flight, by the function of a hidden Markov model used in
bioinformatics or speech recognition or when analyzing the dynamics
of asset pricing provided by the nonlinear models of financial
mathematics. Dynamic Systems Models demonstrates the use of
algorithms based on polynomial approximation which have weaker
requirements than already-popular iterative methods. Specifically,
they do not require a first approximation of a root vector and they
allow non-differentiable elements in the vector functions being
approximated. The text covers all the points necessary for the
understanding and use of polynomial approximation from the
mathematical fundamentals, through algorithm development to the
application of the method in, for instance, aeroplane flight
dynamics or biological sequence analysis. The technical material is
illustrated by the use of worked examples and methods for training
the algorithms are included. Dynamic Systems Models provides
researchers in aerospatial engineering, bioinformatics and
financial mathematics (as well as computer scientists interested in
any of these fields) with a reliable and effective numerical method
for nonlinear estimation and solving boundary problems when
carrying out control design. It will also be of interest to
academic researchers studying inverse problems and their solution.
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