Nonlinear Time Series And Signal Processing (lecture Notes In Control And Information Sciences)

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. On the application of kalman filtering to correct errors due to vertical deflections in inertial navigation....Pages 1-9 Filtering and detection problem for nonlinear time series....Pages 10-17 Spectral and bispectral methods for the analysis of nonlinear (non Gaussian) time series signals....Pages 18-42 Bilinear time series: Theory and application....Pages 43-58 Bivariate bilinear models and their specification....Pages 59-74 Non-linear time series modelling in population biology: A preliminary case study....Pages 75-87 The akaike information criterion in threshold modelling: Some empirical evidences....Pages 88-96 Nonlinear time series analysis for dynamical systems of catastrophe type....Pages 97-118 Nonlinear processing with Mth-order signals....Pages 119-129 Stochastic circulatory lymphocyte models....Pages 130-145