代码搜索：corresponding
找到约 4,250 项符合「corresponding」的源代码
代码结果 4,250
www.eeworm.com/read/158750/10731609
m cplxcomp.m
function I = cplxcomp(p1,p2)
% I = cplxcomp(p1,p2)
% Compares two complex pairs which contain the same scalar elements
% but (possibly) at differrent indices. This routine should be
% used after C
www.eeworm.com/read/273878/10895895
m distance.m
function d = distance(inputcities)
% DISTANCE
% d = DISTANCE(inputcities) calculates the distance between n cities as
% required in a Traveling Salesman Problem. The input argument has two rows
%
www.eeworm.com/read/273787/10901141
m butterd.m
% IIR BUTTERWORTH FILTER DESIGN
%
% [N,D] = BUTTERD(order,passedge)
% It combines the function "BUTTER" and
% "freqplot". Order is the IIR filter
% numeritor polynomial order. Passedge is
% the
www.eeworm.com/read/271940/10975717
m cx.m
% CX means Olives, Smith, and Hollands' Cycle Crossover(CX)
% Procedure :CX
% Step1. Find the cycle which is defined by the corresponding positions of cities between parents
% Step2. Copy the citie
www.eeworm.com/read/271119/11006675
java tablepanel.java
/* TablePanel.java */
import java.awt.*;
import java.applet.*;
import java.io.*;
import java.util.*;
/**
* This class creates a panel with a table canvas and a vertical scrollbar
* to the east of
www.eeworm.com/read/343753/6963606
m lttr1.m
function [w1,b1,tr,rq] = lttr1(w1,b1,f1,...
xc,P,T,VA,VAT,TE,TET,TP)
%LTTR1 Trains a large feed-forward network containing no hidden layers
%using a Tikhonov regularized truncated Gauss-Newton me
www.eeworm.com/read/451678/6964399
m distance.m
function d = distance(inputcities)
% DISTANCE
% d = DISTANCE(inputcities) calculates the distance between n cities as
% required in a Traveling Salesman Problem. The input argument has two rows
%
www.eeworm.com/read/469416/6976350
m netinit.m
function net = netinit(net, prior)
%NETINIT Initialise the weights in a network.
%
% Description
%
% NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
% the weights and biase
www.eeworm.com/read/469416/6976395
m netunpak.m
function net = netunpak(net, w)
%NETUNPAK Separates weights vector into weight and bias matrices.
%
% Description
% NET = NETUNPAK(NET, W) takes an net network data structure NET and a
% weight
www.eeworm.com/read/469416/6976500
m mlpprior.m
function prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)
%MLPPRIOR Create Gaussian prior for mlp.
%
% Description
% PRIOR = MLPPRIOR(NIN, NHIDDEN, NOUT, AW1, AB1, AW2, AB2) generates a