Recursive Estimation: A General Tool in Data Analysis and Stochastic Model Building
In this book, we have attempted to provide an elementary introduction to recursive estimation; an introduction which has concentrated on the development of algorithms and has indicated how these algorithms can be useful in practical applications. Because of its introductory nature, however, much has had to be omitted either to protect the reader from overly esoteric theory at too early a stage in his exposure to the subject or to limit the size of the book in sympathy with the aims of an introductory text. In this final chapter, therefore, we mention some other, related, recursive estimation procedures that have been developed recently and which appear to have good potential for practical application. Together with the recursive algorithms discussed in previous chapters, these additional procedures provide the systems analyst with valuable additions to his ‘tool bag’ of techniques; additions which have utility in all phases of time-series analysis from data processing, through model structure identification and state/parameter estimation, to stochastic model building for linear and nonlinear systems in both well defined and poorly defined situations.
KeywordsStochastic Approximation Recursive Little Square Recursive Estimation Adaptive Loop Optimal State Estimate
Unable to display preview. Download preview PDF.