| Stochastic Approximation and Its Applications 2002 Edition Contributor(s): Han-Fu Chen (Author) |
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ISBN: 1402008066 ISBN-13: 9781402008061 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: August 2002 Annotation: This book presents the recent development of stochastic approximation algorithms with expanding truncations based on the TS (trajectory-subsequence) method, a newly developed method for convergence analysis. This approach is so powerful that conditions used for guaranteeing convergence have been considerably weakened in comparison with those applied in the classical probability and ODE methods. The general convergence theorem is presented for sample paths and is proved in a purely deterministic way. The sample-path description of theorems is particularly convenient for applications. Convergence theory takes both observation noise and structural error of the regression function into consideration. Convergence rates, asymptotic normality and other asymptotic properties are presented as well. Applications of the developed theory to global optimization, blind channel identification, adaptive filtering, system parameter identification, adaptive stabilization and other problems arising from engineering fields are demonstrated. Audience: Researchers and students of both graduate and undergraduate levels in systems and control, optimization, signal processing, communication and statistics. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - General - Mathematics | Linear & Nonlinear Programming - Mathematics | Applied |
| Dewey: 519.2 |
| LCCN: 2002030030 |
| Series: Nonconvex Optimization and Its Applications |
| Physical Information: 0.88" H x 6.14" W x 9.21" (1.56 lbs) 360 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Estimating unknown parameters based on observation data conta- ing information about the parameters is ubiquitous in diverse areas of both theory and application. For example, in system identification the unknown system coefficients are estimated on the basis of input-output data of the control system; in adaptive control systems the adaptive control gain should be defined based on observation data in such a way that the gain asymptotically tends to the optimal one; in blind ch- nel identification the channel coefficients are estimated using the output data obtained at the receiver; in signal processing the optimal weighting matrix is estimated on the basis of observations; in pattern classifi- tion the parameters specifying the partition hyperplane are searched by learning, and more examples may be added to this list. All these parameter estimation problems can be transformed to a root-seeking problem for an unknown function. To see this, let - note the observation at time i. e., the information available about the unknown parameters at time It can be assumed that the parameter under estimation denoted by is a root of some unknown function This is not a restriction, because, for example, may serve as such a function. |