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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