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找到约 5,352 项符合「Learning」的源代码
代码结果 5,352
www.eeworm.com/read/256398/12001796
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/342008/12047605
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/255755/12057196
m ffnc.m
%FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
%
www.eeworm.com/read/341517/12080519
m chap4_1.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/152680/12093714
m chap4_1.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/152580/12100938
m exmulticlass1v1margdif.m
%
% Example MultiClass SVM Classifiction
% "One against One" with Feature Selection
%
%
%
close all
clear all
%--------------------------------------------------
n=50;
sigma=1;
x1=sigma
www.eeworm.com/read/152310/12122639
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/151851/12168739
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/253950/12173322
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