www.uggbootst.com Discrete wavelet transform based

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Discrete wavelet transform based dynamic Markov process model parameter identification method

Discussion of the results can greatly reduce the computational complexity of +1 ... L and improve the numerical stability, thus providing a viable approach. To do this, first transform the data in a wavelet variable ? (, \) ~ (13) as:)) / Z high, \z (15) Total value (22) instead of (on the grounds that, Z is a colored noise with real Rc \= L B (17) output, the difference is the colored noise approximation) that; = (paste CAO \U Based on the above discussion of the wavelet decomposition can be obtained (15) of the recursive algorithm is as follows: coefficient vector,[link widoczny dla zalogowanych], corresponding to B and on U according to equation (9) to form the moment the first step: the use of ordinary least square method to estimate the noise to the array . capacity;. target population: The following constant is assumed (\Screening of the pan to the threshold K (k) a P (k a 1) hr (a 1) h +1] into a phase 2 subjects such as silicon: the dynamic based on discrete wavelet transform of the Markov process model parameter identification method for P ( ) = [a K (k)] P (a 1) when �� 0,, e (10; when k> O,[link widoczny dla zalogowanych], the; () a 2 () a 9 (I 1) Step two: Calculation of input output data Gong, z and the noise vector P of the wavelet transform was u-z \(20) and (21) Find the Cholesky decomposition L L. The fourth step: according to equation (19) Calculate the H and z \i (A 1) + (), Hitomi (again and again (I 1)] weight () = (a 1) h Lu (a 1) h +1] Lo () a [j I ()] (1) where i) , i, respectively, alum of the first k elements and k column vectors. Note 1 The first step of the algorithm and the fifth step is the ordinary least square method, its computational complexity is o (��); second step of the discrete wavelet transform is equivalent to using a �� matrix left by the corresponding data vector, so the complexity is o (��); the third step of the complexity of calculation of a 0 (Nlogz ��), while the type (2O ) and (21) The complexity of computing L O (Nlog ~ N) (proof see [5]); the fourth step from (19) Calculation Date and z, the deletion of the lower triangular matrix L, so the computational complexity degree o (��). In summary, the proposed Markov recursive algorithm, the computational complexity of not more than an ordinary recursive least squares method, so there is a practical application. Note 2 When the data length L greater than the wavelet transform data window length, can be rolling data window, which represents each of the data collected into a new one to throw away the data front, to keep the data window length unchanged. 4 simulation examples to verify the proposed algorithm, in this section were the most common AR model of the three noise model, MA model and ARMA models give an example of each of these examples in the literature [1] were used as test generalized least squares, extended least squares method,[link widoczny dla zalogowanych], the typical multi-stage least squares in the side sub- all cases, use the D4 wavelet, () is the Gauss white noise, input \L = 228. control the input noise to noise ratio of 73 letters. Consider the following second order differential model: 2 () a 1. (1) +0.7 z (a 2) = \) + () Example 1 for the AR model noise model:) a 0.85e (k-1) = () case 2 noise model for the MA model:) =) A v (k a 1) +0.2 v (point 2 ) for the ARMA noise model case 3 model:, ff) +0.9 e A 1) +0.95 e Mantle 2) a () +1.5 v A 1) +0.75 v (k-2) more than three cases, the use of Thought Section of the proposed method,[link widoczny dla zalogowanych], using Matlab programming language, on the PC486 computer calculation of the estimated results shown in Table 1, each of which cases the computing time of about SOs the parameter estimates of the change process as shown in Figure 3 to Figure 5 Fig. shows the algorithm is stable and effective, the identification results

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