代码搜索:Gradient

找到约 2,951 项符合「Gradient」的源代码

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www.eeworm.com/read/136821/5851425

h graphic.h

///////////////////////////////////////////////////////////// // Flash Plugin and Player // Copyright (C) 1998 Olivier Debon // // This program is free software; you can redistribute it and/or // mod
www.eeworm.com/read/135582/5884738

h graphic.h

///////////////////////////////////////////////////////////// // Flash Plugin and Player // Copyright (C) 1998 Olivier Debon // // This program is free software; you can redistribute it and/or // mod
www.eeworm.com/read/456224/6278707

m gradfunction.m

function Return = gradfunction(functname,x) % % numerical computation of gradient % this allows automatic gradient computation % % % first forward finite difference % hstep = 0.001; - programm
www.eeworm.com/read/456224/6278729

m grad_ex6_1.m

function Return = Grad_Ex6_1(functname,x) % % numerical computation of gradient % this allows automatic gradient computation % % % first forward finite difference % hstep = 0.001; - programmed
www.eeworm.com/read/456224/6278841

m gradfunction.m

function Return = gradfunction(functname,x) % % numerical computation of gradient % this allows automatic gradient computation % % % first forward finite difference % hstep = 0.001; - programm
www.eeworm.com/read/382404/6302691

txt bp神经网络实例.txt

例1 采用动量梯度下降算法训练 BP 网络。 训练样本定义如下: 输入矢量为 p =[-1 -2 3 1 -1 1 5 -3] 目标矢量为 t = [-1 -1 1 1] 解:本例的 MATLAB 程序如下: close all clear echo on clc % NEWFF——生成
www.eeworm.com/read/338062/6311158

m shili24.m

function shili24 h0=figure('toolbar','none',... 'position',[200 150 450 350],... 'name','实例24'); subplot(2,2,1) z=peaks; ribbon(z) title('Figure1') subplot(2,2,2) [x,y,z]=peaks(15);
www.eeworm.com/read/407117/6350340

txt bp神经网络实例.txt

例1 采用动量梯度下降算法训练 BP 网络。 训练样本定义如下: 输入矢量为 p =[-1 -2 3 1 -1 1 5 -3] 目标矢量为 t = [-1 -1 1 1] 解:本例的 MATLAB 程序如下: close all clear echo on clc % NEWFF——生成
www.eeworm.com/read/493397/6402206

html lucasperso.html

www.eeworm.com/read/491859/6427867

m li38.m

v=-2:0.2:2; [x,y]=meshgrid(v); z=x.*exp(-x.^2-y.^2); [px,py]=gradient(z,.2,.2); contour(v,v,z); hold on; quiver(v,v,px,py); hold off;