| Summary: | 碩士 === 國立交通大學 === 控制工程系 === 82 === In this thesis, two modeling methodologies are developed for
the identification of discrete-time chaotic systems. One of
thodologies is linear stochastic modeling utilizing the model-
based spectral estimation to find a spectrum-approximated
Markovian model. The approach is related to a Markovian
representation such that the power spectrum of the estimated
output is very close to the power spectrum of the original one.
This approach is that by solvingdiscrete-time algebraic Riccati
equation associated with the system matrices of the Markovian
model, we can have the system dynamics. The novelty of this
approach is to get system dynamics systematically and much
easier. The other modeling methodologies is nonlinear
deterministic modeling using neural networks to model a
discrete-time chaotic system. A Series-Parallel Model can
decribe the system dynamics but its tracking data must always
be allowable. A Parallel Model can hold partial system
dynamics, but it can't track data well. A new model, called
Novel Model,can overcome disadvantages of the Parallel Model
and the Series-Parallel Model.
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