代码搜索：Learning
找到约 5,352 项符合「Learning」的源代码
代码结果 5,352
www.eeworm.com/read/395557/8168251
m modifiedpid.m
%Single Neural Adaptive PID Controller
clear all;
close all;
x=[0,0,0]';
xiteP=0.40;
xiteI=0.35;
xiteD=0.40;
%Initilizing kp,ki and kd
wkp_1=0.10;
wki_1=0.10;
wkd_1=0.10;
%wkp_1=rand;
www.eeworm.com/read/294886/8195860
m contents.m
% Neural Network Design Demonstrations.
% Copyright (c) 1994 by PWS Publishing Company.
%
% General
% nnd - Splash screen.
% nndtoc - Table of contents.
% nnsound - Turn Neural Net
www.eeworm.com/read/393865/8257791
m rncalc.m
function [c,d]=rncalc(xapp,yapp,kernel,kerneloption,lambda,T)
% USAGE
%
% [c,d]=rncalc(xapp,app,kernel,kerneloption,lambda,T);
%
%
% y= K*c+ T*d
% calculates the minimizer of
www.eeworm.com/read/293183/8310890
m learnbpm.m
function [dw,db] = learnbpm(p,d,lr,mc,dw,db)
%LEARNBPM Backpropagation learning rule with momentum.
%
% [dW,dB] = LEARNBPM(P,D,LR,MC,dW,dB)
% P - RxQ matrix of input vectors.
% D - SxQ matrix o
www.eeworm.com/read/367442/9747852
m contents.m
% Statistical Pattern Recognition Toolbox.
%
% Contents
%
% bayes - (dir) Bayes classification.
% datasets - (dir) Functions for handling with data sets.
% generalp - (dir) General purpose
www.eeworm.com/read/367442/9747888
m contents.m
% Statistical learning methods.
%
% Included directories (implementing algorithms):
% minimax - (dir) Minimax learning algorithm.
% unsuper - (dir) Unsupervised learning methods, EM algori
www.eeworm.com/read/170936/9779152
m demolgd1.m
%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling X
www.eeworm.com/read/415313/11076371
m demolgd1.m
%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling X
www.eeworm.com/read/269482/11097266
java repaintexample.java
import java.awt.*;
import java.applet.*;
public class RepaintExample extends Applet {
int x;
public void init(){
x=5;
}
public void paint(Graphics g) {
x=x+10;
if (x>=200) x=5;
www.eeworm.com/read/413912/11137092
m demolgd1.m
%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by sampling X