hosa

所属分类:汇编语言
开发工具:matlab
文件大小:97KB
下载次数:275
上传日期:2009-03-25 09:56:57
上 传 者hlbit
说明:  关于高阶累积量分析的Matlab函数,对初学高阶累积量者比较有用。
(Matlab function for Higher-Order Spectral Analysis (HOSA) )

文件列表:
hosa\armaqs.m (4497, 1999-01-29)
hosa\armarts.m (2907, 1999-01-29)
hosa\armasyn.m (4089, 1999-01-29)
hosa\arorder.m (4209, 1999-01-29)
hosa\arrcest.m (4078, 1999-01-29)
hosa\biceps.m (5493, 1999-01-29)
hosa\bicepsf.m (5183, 1999-01-29)
hosa\bicoher.m (4250, 1999-01-29)
hosa\bicoherx.m (4762, 1999-01-29)
hosa\bispecd.m (5899, 1999-01-29)
hosa\bispecdx.m (6552, 1999-01-29)
hosa\bispeci.m (5288, 1999-01-29)
hosa\bispect.m (2019, 1999-01-29)
hosa\Contents.m (4972, 1999-01-29)
hosa\cum2est.m (2113, 1999-01-29)
hosa\cum2x.m (3177, 1999-01-29)
hosa\cum3est.m (2946, 1999-01-29)
hosa\cum3x.m (3655, 1999-01-29)
hosa\cum4est.m (4507, 1999-01-29)
hosa\cum4x.m (5725, 1999-01-29)
hosa\cumest.m (2785, 1999-01-29)
hosa\cumtrue.m (4537, 1999-01-29)
hosa\doa.m (5976, 1999-01-29)
hosa\doagen.m (5626, 1999-01-29)
hosa\glstat.m (8698, 1999-01-29)
hosa\harmest.m (6533, 1999-01-29)
hosa\harmgen.m (3778, 1999-01-29)
hosa\hosahelp.m (4258, 1999-01-29)
hosa\hprony.m (3215, 1999-01-29)
hosa\ivcal.m (2162, 1999-01-29)
hosa\maest.m (3418, 1999-01-29)
hosa\maorder.m (2959, 1999-01-29)
hosa\matul.m (2884, 1999-01-29)
hosa\nlgen.m (2313, 1999-01-29)
hosa\nlpow.m (3956, 1999-01-29)
hosa\nltick.m (4655, 1999-01-29)
hosa\pickpeak.m (2765, 1999-01-29)
hosa\qpcgen.m (5769, 1999-01-29)
hosa\qpctor.m (4728, 1999-01-29)
... ...

% README file for the HOSA Toolbox. % Version 2.0.3 (R11) 10-Jul-19*** % % Note: There have been no changes in Toolbox functionality. % ********************************************************************* % Bug fixes for version 2.0.3: % % 1) CUM4X - Corrected conjugation errors related to the computation of % R_wy, R_zy and M_yx. % % 2) TDE - Corrected a size error when the 'svdflag' input argument was % used. % % ********************************************************************* % The HOSA Manual: % % The classification example in the Case Studies section of the Higher % Order Spectral Analysis Toolbox manual does not define x and y. % Users cannot run the example because they do not have the two % underwater acoustic signals. % ********************************************************************* % Changes to the Toolbox for version 2.0.2: % % 1) There have been no changes in Toolbox functionality. Several % of the demo M-files have been modified. In particular, each demo % can be invoked separately without going through the HOSADEM or % HOSADEMO functions. % % 2) Command-line demos will now plot to only one figure window. % % 3) Case-study demo figures will be closed upon completion of each % case study. % % 4) The matul function has been changed to correct a bug. % The function now produces the correct coefficient matrix % based on the Matsuoka-Ulrych paper. % % 5) The harmest, doa, tde, and qpctor functions have been modified % to validate user-entered order. % % 6) The hprony function has been modified to fix incompatibilities % with the toeplitz function. % % 7) Several other minor changes (i.e. add grid lines) % % ********************************************************************* % The HOSA Manual: % % The classification example in the Case Studies section of the Higher % Order Spectral Analysis Toolbox manual does not define x and y. % Users cannot run the example because they do not have the two % underwater acoustic signals. %********************************************************************** % % Changes to the HOSA Manual % A bug in routine glstat.m has been fixed; this leads to % changes in the output of glstat.m; HOSA manual pages should % be corrected as shown below (none of the interpretations change) % % On p 1-21,1-22: % % glstat(g,0.51,256) % Test statistic for Gaussianity is 22.179 with df = 48, Pfa = 0.9995 % Linearity test: % R (estimated) = 0.88819, lambda = 0.68932, R (theory) = 2.9288, N = 14 % % glstat(u,0.51,256) % Test statistic for Gaussianity is 17.4885 with df = 48, Pfa = 1 % Linearity test: % R (estimated) = 0.72383, lambda = 0.51704, R (theory) = 2.7453, N = 14 % % glstat(e,0.51,256) % Test statistic for Gaussianity is 253.3529 with df = 48, Pfa = 0 % Linearity test: % R (estimated) = 7.8894, lambda = 9.4555, R (theory) = 8.4655, N = 14 % % glstat(x,0.51,256) % Test statistic for Gaussianity is 277.5194 with df = 48, Pfa = 0 % Linearity test: % R (estimated) = 6.7513, lambda = 10.6519, R (theory) = 8.968, N = 14 % % glstat(z,0.51,256) % Test statistic for Gaussianity is 12***0.0657 with df = 48, Pfa = 0 % Linearity test: % R (estimated) = 606.9323, lambda = 492.5759, R (theory) = 59.9088, N = 14 % % glstat(l,0.51,256) % Test statistic for Gaussianity is 49.931 with df = 48, Pfa = 0.3965 % Linearity test: % R (estimated) = 2.6047, lambda = 1.8124, R (theory) = 4.0038, N = 14 % % p 1-96 (sunspot data) % % Test statistic for Gaussianity is 357.4639 with df = 60, Pfa = 0 % Linearity test: % R (estimated) = 14.8592, lambda = 11.0332, R (theory) = 9.1222, N = 16 % % p 1-*** (sunspot data, differenced) % % Test statistic for Gaussianity is 250.1965 with df = 70, Pfa = 0 % Linearity test: % R (estimated) = 13.5335, lambda = ***449, R (theory) = 7.0433, N = 16 % % % p 1-101 (canadian lynx data) % % Test statistic for Gaussianity is 196.752 with df = 28, Pfa = 0 % Linearity test: % R (estimated) = 6.8468, lambda = 11.299, R (theory) = 9.2282, N = 5 % % p 1-109 (laughter data) % % Test statistic for Gaussianity is 71.3231 with df = 48, Pfa = 0.0161 % Linearity test: % R (estimated) = 2.3216, lambda = 2.376, R (theory) = 4.472, N = 14 % %********************************************************************** % % The classification example in the Case Studies section of the Higher % Order Spectral Analysis Toolbox manual does not define x and y. % Users cannot run the example because they do not have the two % underwater acoustic signals. % % % The laughter example code in the Case Studies section of the doc % has an error: Change the two lines following % % --------------------- power spectra and cum-4 spectra % to % figure(3), [px2,a21,a22] = harmest(sp,25,12,'biased',512,2); % figure(4), [px4,a41,a42] = harmest(sp,25, 8,'biased',512,4); % % % The example for estimating cumulants in the Polyspectra and Linear % Processes section of the manual has a syntax error in its use of the % contour command. The arguments were in the wrong order. Change the % line to read as the following and it will work fine. % subplot(122), contour(-n:n,-n:n,cmat,8) % %********************************************************************** % Copyright (c) 1991-1999 by United Signals & Systems, Inc. and The Mathworks, Inc. All Rights Reserved. % $Revision: 1.10 $ % A. Swami November 21, 1997 % RESTRICTED RIGHTS LEGEND % Use, duplication, or disclosure by the Government is subject to % restrictions as set forth in subparagraph (c) (1) (ii) of the % Rights in Technical Data and Computer Software clause of DFARS % 252.227-7013. % Manufacturer: United Signals & Systems, Inc., P.O. Box 2374, % Culver City, California 90231. % % This material may be reproduced by or for the U.S. Government pursuant % to the copyright license under the clause at DFARS 252.227-7013. disp('HOSA Toolbox Version 2.0.3 (R11) 10-Jul-19***') disp('Press any key to see readme file'),pause clc, help readme

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