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核自适应滤波KAF备份

于 2020-08-07 发布
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下载积分: 1 下载次数: 4

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说明:  适用于初学者练习和入门,里面有几种基础算法的源码和练习版本,需要对照书去学习(Suitable for beginners and beginners, there are several basic algorithm source code and exercise version, need to learn the reference book)

文件列表:

核自适应滤波KAF备份\src, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch2_codes, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch2_codes\channelEq, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch2_codes\channelEq\PART1.m, 2526 , 2016-08-08
核自适应滤波KAF备份\src\ch2_codes\channelEq\PART2.m, 3968 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction, 0 , 2020-07-29
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\gramMatrix.m, 714 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\ker_eval.m, 752 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\KLMS1.m, 2143 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\KLMS1_LC.m, 2866 , 2009-02-07
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\KLMS3.m, 3327 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\LMS1.m, 1454 , 2020-07-08
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\MK30.mat, 37821 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART1.m, 2449 , 2020-07-29
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART10.m, 4385 , 2009-02-07
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART2.m, 4056 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART3.m, 2750 , 2020-06-09
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART4.m, 4666 , 2009-05-17
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART5.m, 5051 , 2009-05-17
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART6.m, 5173 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART7.m, 5052 , 2009-05-17
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART8.m, 4027 , 2009-05-17
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\PART9.m, 7351 , 2009-05-17
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\regularizationNetwork.m, 1579 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\sparseKLMS1.m, 3907 , 2008-10-19
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\Study1LMS1.m, 585 , 2020-06-05
核自适应滤波KAF备份\src\ch2_codes\mg_prediction\Study2LMS.m, 174 , 2020-06-06
核自适应滤波KAF备份\src\ch2_codes\regularization_function, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch2_codes\regularization_function\regularizationfuntion.m, 2102 , 2009-05-17
核自适应滤波KAF备份\src\ch3_codes, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch3_codes\channelEq, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch3_codes\channelEq\APA1.m, 2160 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\APA1s.m, 1858 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\gramMatrix.m, 714 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\ker_eval.m, 689 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\LMS1.m, 2049 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\LMS1s.m, 1705 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\LMS2.m, 2163 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\PART1.m, 8351 , 2009-05-18
核自适应滤波KAF备份\src\ch3_codes\channelEq\PART2.m, 9302 , 2009-05-18
核自适应滤波KAF备份\src\ch3_codes\channelEq\PART3.m, 5888 , 2009-05-18
核自适应滤波KAF备份\src\ch3_codes\channelEq\sparseKAPA1.m, 4866 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\sparseKAPA1s.m, 4207 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\sparseKAPA2.m, 5095 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\sparseKAPA2s.m, 4443 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\sparseKLMS1.m, 4144 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\channelEq\sparseKLMS1s.m, 3635 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\gramMatrix.m, 714 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\KAPA1.m, 4217 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\KAPA2.m, 4454 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\ker_eval.m, 689 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\KLMS1.m, 2863 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\KRLS.m, 3093 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\LMS1.m, 2049 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\MK30.mat, 37821 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\PART1.m, 6174 , 2009-05-17
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\PART2.m, 7571 , 2009-05-18
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\slidingWindowKRLS.m, 3632 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\sparseKAPA1.m, 4626 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\sparseKAPA2.m, 4870 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\mg_prediction\sparseKLMS1.m, 3907 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\noiseCancelation, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch3_codes\noiseCancelation\fmri.mat, 1580350 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\noiseCancelation\gramMatrix.m, 714 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\noiseCancelation\ker_eval.m, 689 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\noiseCancelation\LMS2.m, 2395 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\noiseCancelation\PART1.m, 5662 , 2009-05-17
核自适应滤波KAF备份\src\ch3_codes\noiseCancelation\PART2.m, 4786 , 2009-05-17
核自适应滤波KAF备份\src\ch3_codes\noiseCancelation\sparseKAPA2.m, 4393 , 2008-10-19
核自适应滤波KAF备份\src\ch3_codes\noiseCancelation\sparseKLMS1.m, 3517 , 2008-10-19
核自适应滤波KAF备份\src\ch4_codes, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch4_codes\channelEq, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch4_codes\channelEq\gramMatrix.m, 714 , 2008-10-19
核自适应滤波KAF备份\src\ch4_codes\channelEq\ker_eval.m, 689 , 2008-10-19
核自适应滤波KAF备份\src\ch4_codes\channelEq\KRLS_ALDs.m, 3705 , 2009-08-08
核自适应滤波KAF备份\src\ch4_codes\channelEq\PART1.asv, 3857 , 2009-08-10
核自适应滤波KAF备份\src\ch4_codes\channelEq\PART1.m, 3834 , 2009-08-10
核自适应滤波KAF备份\src\ch4_codes\channelEq\PART3.asv, 3740 , 2009-08-08
核自适应滤波KAF备份\src\ch4_codes\channelEq\PART3.m, 3945 , 2009-08-10
核自适应滤波KAF备份\src\ch4_codes\channelEq\sparseKLMS1.m, 4144 , 2008-10-19
核自适应滤波KAF备份\src\ch4_codes\channelEq\sparseKLMS1s.asv, 3639 , 2009-08-08
核自适应滤波KAF备份\src\ch4_codes\channelEq\sparseKLMS1s.m, 3693 , 2009-08-08
核自适应滤波KAF备份\src\ch4_codes\gpr, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml, 0 , 2020-06-04
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\approxEP.m, 5097 , 2007-07-24
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\approximations.m, 1936 , 2007-06-27
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\approxLA.m, 3094 , 2007-06-26
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\binaryEPGP.m, 2671 , 2007-06-26
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\binaryGP.m, 6941 , 2007-06-27
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\binaryLaplaceGP.m, 3071 , 2007-06-26
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\Contents.m, 2656 , 2007-06-26
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\Copyright, 776 , 2007-06-26
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\covConst.m, 774 , 2007-07-24
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\covFunctions.m, 4136 , 2006-05-15
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\covLINard.m, 1046 , 2006-03-27
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\covLINone.m, 984 , 2006-03-27
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\covMatern3iso.m, 1392 , 2007-06-26
核自适应滤波KAF备份\src\ch4_codes\gpr\gpml-matlab\gpml\covMatern5iso.m, 1417 , 2007-06-26

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    维纳滤波器(Wiener filter)是由数学家维纳(Rorbert Wiener)提出的一种以最小平方为最优准则的线性滤波器。在一定的约束条件下,其输出与一给定函数(通常称为期望输出)的差的平方达到最小,通过数学运算最终可变为一个托布利兹方程的求解问题。维纳滤波器又被称为最小二乘滤波器或最小平方滤波器,目前是基本的滤波方法之一。(Wiener filter (Wiener filter) is caused by a mathematician Wiener (Rorbert Wiener) proposed guidelines for the minimum square optimal linear filter. Under certain constraints, which outputs a given function (often called the desired output) reaches the minimum squared differential, the final mathematical operation may become a problem solving Equation Leeds Taub. Wiener filter is also known as the least squares filter or least squares filter, currently one of the basic filtering methods.)
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