代码搜索：multidimensional
找到约 559 项符合「multidimensional」的源代码
代码结果 559
www.eeworm.com/read/454660/7385994
java ex20(2).java
// arrays/Ex20.java
// TIJ4, Chapter Arrays, Exercise 20, page 778
// Demonstrate deepEquals() for multidimensional arrays.
import java.util.*;
import static net.mindview.util.Print.*;
class A
www.eeworm.com/read/454559/7387347
m lms.m
function [W, e] = lms(u, d, mu, decay, verbose)
% function [W, e] = lms(u, d, mu, decay, verbose)
%
% lms.m - use multidimensional LMS algorithm to predict AR process
% written for MATLAB 4.0
%
www.eeworm.com/read/448526/7532065
m lms.m
function [W, e] = lms(u, d, mu, decay, verbose)
% function [W, e] = lms(u, d, mu, decay, verbose)
%
% lms.m - use multidimensional LMS algorithm to predict AR process
% written for MATLAB 4.0
%
www.eeworm.com/read/434781/7801959
java ex20(2).java
// arrays/Ex20.java
// TIJ4, Chapter Arrays, Exercise 20, page 778
// Demonstrate deepEquals() for multidimensional arrays.
import java.util.*;
import static net.mindview.util.Print.*;
class A
www.eeworm.com/read/298615/7949334
m lms.m
function [W, e] = lms(u, d, mu, decay, verbose)
% function [W, e] = lms(u, d, mu, decay, verbose)
%
% lms.m - use multidimensional LMS algorithm to predict AR process
% written for MATLAB 4.0
%
www.eeworm.com/read/333003/12712075
java ex20(2).java
// arrays/Ex20.java
// TIJ4, Chapter Arrays, Exercise 20, page 778
// Demonstrate deepEquals() for multidimensional arrays.
import java.util.*;
import static net.mindview.util.Print.*;
class A
www.eeworm.com/read/140957/13050748
m lms.m
function [W, e] = lms(u, d, mu, decay, verbose)
% function [W, e] = lms(u, d, mu, decay, verbose)
%
% lms.m - use multidimensional LMS algorithm to predict AR process
% written for MATLAB 4.0
%
www.eeworm.com/read/313151/13595253
java ex20(2).java
// arrays/Ex20.java
// TIJ4, Chapter Arrays, Exercise 20, page 778
// Demonstrate deepEquals() for multidimensional arrays.
import java.util.*;
import static net.mindview.util.Print.*;
class A
www.eeworm.com/read/312533/13609983
m lms.m
function [W, e] = lms(u, d, mu, decay, verbose)
% function [W, e] = lms(u, d, mu, decay, verbose)
%
% lms.m - use multidimensional LMS algorithm to predict AR process
% written for MATLAB 4.0
%
www.eeworm.com/read/302650/13829499
m detect.m
function varargout = detect(sig_down,dlt,slt,ch_coefs,varargin)
%DETECT Multidimensional data detector.
% D_E = DETECT(S,DLT,SLT,ALPHA) performs the maximum likelihood
% sequence estimation (MLSE