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Additional resources for Modern Control Theory (3rd Edition)
Sample text
1 . 74) N o w i n vert th i s eq u a t i on. 74 : MK � k + 1 = Z'[Q; I - Q; I (Qk- 1 + Rr l /lM (2. 76) + Th is equat i o n desc r i bes the propaga t i o n of t he info rmat ion bet \\'een m e a s l l re m e n I s . ma t ri x U I'f)ATI�G T i l E I N FO R M ATlO:-l MATRIX. The i n fo rmation m a trix, after the quantized measurement has been processe d . � k = Mi:� k x + MK � kKK - k(Ci:� h K[ - k MK2k . 72. 78) to - Eq uation 2. Ka: - . = Kf- tMi ! Ka: - . 69 H[ri! 80) ri! 8 1 ) This equation describes the a posteriori information matrix Ei.
Pli... 5 Signal-to-noisc for the Gaussian fit algorithm: predictive quantization with binary quantizer. 736 for for,. = 20). 9988 quite accurate. 6 contains the outcomes for data-compression results system. :/(}�, the ratio of threshold hal [width to a priori standard deviation. Note the cxcellent agreement bctween perform ance estimates and simulation results. of the shown as 30 I. PREDICTiVE· COMPARISON DATA COMPRESSION c 25 Q m o �OO 20 z o . Q � � � '" � is U z 2 ;t Z : 10 ':2 � • 5 THRHHOtD-HALFWIOTHI A.
They consider various memory lengths and a binary quantizer, an d here we use a growing memory (finite storage) and ar b itrary quantizers. Although the Gaus s i an fit algorithm and its performance estimate may be used on nonstationary da ta, only sta ti on ary data have been simulated as yet. Simulatioll Descriptioll IIlPlit Process The second-order, Gauss-Markov input proces s is the sampled output of a linear sy s t em driven by Gaussian white noise. 1 7) where (he gain c is so chosen as to provide (he proper variance at the output.