代码搜索:Generalized

找到约 2,645 项符合「Generalized」的源代码

代码结果 2,645
www.eeworm.com/read/425643/10342448

m xnewgrnn.m

function xNewgrnn % xNewgrnn.m % 函数逼近(function approximation)--用函数NEWGRNN()和SIM()创建和仿真 % 普遍化回归神经网络(generalized regression neural network,GRNN) % % Author: HUANG Huajiang % Copyright 2003 U
www.eeworm.com/read/301544/6962357

m ggmle.m

function [mhat, ahat, bhat] = ggmle(x, options) % GGMLE Parameter estimates for generalized Gaussian distributed data. % % [MHAT, AHAT, BHAT] = GGMLE(X, OPTIONS) % % Returns the maxi
www.eeworm.com/read/467252/7008250

m estpdf.m

function [alpha, beta, K] = estpdf(m1, m2) % ESTPDF Estimate generalized Gaussian p.d.f of a wavelet subband % % Input: % m1: average absolute values of subband coefficients % m2: variances of su
www.eeworm.com/read/449771/7496807

m xnewgrnn.m

function xNewgrnn % xNewgrnn.m % 函数逼近(function approximation)--用函数NEWGRNN()和SIM()创建和仿真 % 普遍化回归神经网络(generalized regression neural network,GRNN) % % Author: HUANG Huajiang % Copyright 2003 U
www.eeworm.com/read/199851/7818647

m xnewgrnn.m

function xNewgrnn % xNewgrnn.m % 函数逼近(function approximation)--用函数NEWGRNN()和SIM()创建和仿真 % 普遍化回归神经网络(generalized regression neural network,GRNN) % % Author: HUANG Huajiang % Copyright 2003 U
www.eeworm.com/read/299459/7849043

m contents.m

% Statistical Pattern Recognition Toolbox (STPRtool). % Version 2.03 14-Dec-2004 % % Bayesian classification. % bayescls - Bayesian classifier with reject option. % bayesdf
www.eeworm.com/read/299459/7849359

html contents.html

Contents.m
www.eeworm.com/read/299459/7849686

html contents.html

Contents.m
www.eeworm.com/read/299459/7850925

m~ contents.m~

% Statistical Pattern Recognition Toolbox (STPRtool). % Version 2.01 27-Aug-2004 % % Bayesian classification. % bayescls - Bayesian classifier with reject option. % bayesdf
www.eeworm.com/read/198545/7929141

m laguerre.m

function [t,w]=laguerre(n,alpha) %LAGUERRE Calculate nodes and weights for generalized laguerre % quadrature. [T W]=LAGUERRE(N,ALPHA) puts N nodes in T and % corresponding weights in W so that the