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Recursive Identification and Parameter Estimation (Hardcover)
Loot Price: R4,382
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Recursive Identification and Parameter Estimation (Hardcover)
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Recursive Identification and Parameter Estimation describes a
recursive approach to solving system identification and parameter
estimation problems arising from diverse areas. Supplying rigorous
theoretical analysis, it presents the material and proposed
algorithms in a manner that makes it easy to understand-providing
readers with the modeling and identification skills required for
successful theoretical research and effective application. The book
begins by introducing the basic concepts of probability theory,
including martingales, martingale difference sequences, Markov
chains, mixing processes, and stationary processes. Next, it
discusses the root-seeking problem for functions, starting with the
classic RM algorithm, but with attention mainly paid to the
stochastic approximation algorithms with expanding truncations
(SAAWET) which serves as the basic tool for recursively solving the
problems addressed in the book. The book not only identifies the
results of system identification and parameter estimation, but also
demonstrates how to apply the proposed approaches for addressing
problems in a range of areas, including: Identification of ARMAX
systems without imposing restrictive conditions Identification of
typical nonlinear systems Optimal adaptive tracking Consensus of
multi-agents systems Principal component analysis Distributed
randomized PageRank computation This book recursively identifies
autoregressive and moving average with exogenous input (ARMAX) and
discusses the identification of non-linear systems. It concludes by
addressing the problems arising from different areas that are
solved by SAAWET. Demonstrating how to apply the proposed
approaches to solve problems across a range of areas, the book is
suitable for students, researchers, and engineers working in
systems and control, signal processing, communication, and
mathematical statistics.
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