Adaptive prediction and predictive control by P.P. Kanjilal

By P.P. Kanjilal

This ebook is set prediction and keep watch over of techniques that are expressed via discrete-time versions (i.e. the features fluctuate ultimately with time). the purpose of the e-book is to supply a unified and entire assurance of the foundations, views and strategies of adaptive prediction, that's utilized by scientists and researchers in a large choice of disciplines

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The sampling theorem A data sequence or signal may contain a number of sinusoidal components. e. fQ £ 2/ m ). In other words, if a continuous time signal is sampled at a frequency / s , the sampled signal will contain all the frequency components of the original signal which are less or equal to fc = / s / 2 . The frequency fc is called the Nyquist critical frequency, and / s is called the Nyquist rate of sampling. 5 Models based on Frequency Domain Analysis 45 There are two basic implications of the sampling theorem: (i) Frequency components lower than or equal to fc can only be used to form the original signal.

2. Models based on orthogonal transformations In this book there is particular emphasis on the use of singular value decomposition (SVD) for modelling. 6). 3). The principle of modelling for the nearly periodic series is that the consecutive periods are aligned into consecutive rows of a matrix, which is SV-decomposed; the decomposed components are now modelled, typically as a time series. 8. A quasiperiodic series, can be decomposed into components which are individually nearly periodic, and hence can be modelled the same way as above.

Bmq"m; d is the time delay between the input u and the output y, that is a change in u results in a change in the output y after d time-steps. ARMAX and CARMA or Controlled ARMA models are of the same category. e. Controlled ARIMA) model has the same structure; the use of CARIMA models in process control features in Chapters 12 and 13. Remarks (1) The models discussed here are algebraically similar to the regression model y(k) » a o + a1x1(k) + ... + a ^ d c ) + e(k). Here, each regressor vector is a time series by itself.

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