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This monograph provides a sample of relevant new results on
dynamical nonlinear statistical modeling and estimation which forms
a basis for more effective signal processing, decision and control.
While the research literature is rich in linear Gaussian
methodologies, new contributions to the most relevant area of
nonlinear and non-Gaussian processes have been scarce. Among the
significant areas of application for which such methodologies are
needed are: economics, biology, immunology, underwater acoustics,
electric power generation, chemical process control, and variable
structure systems in general. The latter include adaptive,
intelligent, and decomposing mathematical structures or processes.
The volume includes ten research papers on theory, computational
methods, and applications. Topics include filtering with
application to inertial navigation, structural-change detection,
bilinear time-series models, bispectral estimation, threshold
models, catastrophic models and a generalized eigenstructure
method.
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R398
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