By P.P. Kanjilal
This e-book is set prediction and keep an eye on of strategies which might be expressed through discrete-time versions (i.e. the features range indirectly with time). the purpose of the publication is to supply a unified and complete assurance of the rules, views and strategies of adaptive prediction, that is utilized by scientists and researchers in a large choice of disciplines
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Extra resources for Adaptive prediction and predictive control
The number of samples per unit time) is of fundamental significance for any discrete time representation. 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.
P(x) . 23 Stochastic Processes 21 Thus the mean and the covariance uniquely describe the Gaussian probability density function. 6) are jointly of Gaussian or normal distribution. 6) is completely specified by the two positive parameters, the mean and the autocorrelation function, given by £
The main considerations are: (a) whether a periodic or quasiperiodic model is desired, or whether the model is to be nonperiodic in nature, (b) whether a linear or a nonlinear model is to be designed, (c) whether one or more variables of interest are inaccessible or unmeasurable, etc. (d) whether the model is to be deterministic or stochastic in nature. Remarks (a) The modelling exercise is greatly simplified if the process is linear or periodic and stationary. In real life, most processes are not so, and efforts are made to preprocess the data to increase the degree of stationarity, periodicity or linearity prior to modelling.
Adaptive prediction and predictive control by P.P. Kanjilal