Adaptive filtering prediction and control by Graham C Goodwin PDF

By Graham C Goodwin

ISBN-10: 0486469328

ISBN-13: 9780486469324

This unified survey of the speculation of adaptive filtering, prediction, and regulate makes a speciality of linear discrete-time structures and explores the common extensions to nonlinear platforms. in line with the significance of pcs to functional purposes, the authors emphasize discrete-time structures. Their process summarizes the theoretical and functional elements of a giant type of adaptive algorithms.
Ideal for complex undergraduate and graduate periods, this therapy comprises components. the 1st part issues deterministic structures, masking versions, parameter estimation, and adaptive prediction and keep an eye on. the second one half examines stochastic platforms, exploring optimum filtering and prediction, parameter estimation, adaptive filtering and prediction, and adaptive keep watch over. broad appendices provide a precis of correct history fabric, making this quantity principally self-contained. Readers will locate that those theories, formulation, and purposes are with regards to various fields, together with biotechnology, aerospace engineering, machine sciences, and electric engineering. 

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Example text

For example, no restriction has been imposed on the nature of the data [in particular, $(t) need not necessarily be bounded]. Property (i) ensures that 8(t) is never further from 8, than 8(0) is. Property (ii) implies that the modeling error, e(t), when appropriately normalized is square summable. This turns out to be a sufficient condition to establish global convergence of an important class of adaptive control algorithms. Property (e) shows that the parameter estimates get closer together as t 00.

B, DR(q) = aoq" ' * * * Naturally, a, can be taken to be nonzero by choice of the power n. ) The degree of NR(q)is chosen as (n - l), or less, to ensure that the corresponding transfer function, H(z), is strictly proper, where Subject to the conditions above, a right difference operator representation is controllable in a sense that we now make precise by relating the model to a controllable state-space model. 7) can be written in the form + L(q)lz(t)= u(t>; [a0qn t 20 where U q )= M 4 ) v(q)T =[q-l E=[a, q - 2 ...

Iii) dr,",(z)l L dr,[Mz)l where dr,[N,(z)] denotes the degree of the ith row of N,(z). Proof. (i) We can always construct a right difference operator representation [D,(q), N,(q)] giving rise to the same transfer function by making DR(z) diagonal, with iith element being the least common denominator of the ith column of H(z), Sec. 3 Difference Operator Representations 33 and then to form NR(z) from the appropriate numerator polynomials. Note that DR(z) and NR(z) formed in this way will not, in general, be relatively right prime.

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Adaptive filtering prediction and control by Graham C Goodwin

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