文件列表:

NN_RBF\BP-RBF-建模\BP\BP.m (8175, 2004-03-06)
NN_RBF\BP-RBF-建模\BP\BP建模.m (6454, 2004-03-06)
NN_RBF\BP-RBF-建模\BP\BP预测.m (7657, 2004-03-08)
NN_RBF\BP-RBF-建模\MLR建模.m (6130, 2004-03-05)
NN_RBF\BP-RBF-建模\MLR预测.m (7631, 2004-03-05)
NN_RBF\BP-RBF-建模\RBF\11.m (2563, 2003-12-29)
NN_RBF\BP-RBF-建模\RBF\22.m (2660, 2003-12-29)
NN_RBF\BP-RBF-建模\RBF\33.m (6341, 2003-12-30)
NN_RBF\BP-RBF-建模\RBF\RBF建模.m (6117, 2004-03-06)
NN_RBF\BP-RBF-建模\RBF\RBF预测.m (2024, 2004-03-06)
NN_RBF\BP-RBF-建模\RBF\建模.m (951, 2003-12-28)
NN_RBF\BP-RBF-建模\RBF\建模1.m (2653, 2003-12-29)
NN_RBF\BP-RBF-建模\主元处理.m (10569, 2003-12-20)
NN_RBF\BP-RBF-建模\原始数据集.m (6758, 2003-12-20)
NN_RBF\BP-RBF-建模\大曲原始数据.m (4868, 2003-12-20)
NN_RBF\BP-RBF-建模\大曲归一化.m (6461, 2003-12-20)
NN_RBF\BP-RBF-建模\大曲预处理后数据.m (6971, 2003-12-20)
NN_RBF\BP-RBF-建模\建模数据集.m (8357, 2003-12-20)
NN_RBF\BP-RBF-建模\数据\PCA降维.m (4847, 2004-03-01)
NN_RBF\BP-RBF-建模\数据\主元处理.m (8136, 2003-12-20)
NN_RBF\BP-RBF-建模\数据\原始数据集.m (4809, 2003-12-20)
NN_RBF\BP-RBF-建模\数据\建模数据集.m (3565, 2004-03-01)
NN_RBF\BP-RBF-建模\数据\归一化后的数据集.m (4523, 2003-12-20)
NN_RBF\BP-RBF-建模\数据\归一后建模数据集.m (6405, 2003-12-20)
NN_RBF\BP-RBF-建模\数据\归一后校验数据集.m (1599, 2003-12-20)
NN_RBF\BP-RBF-建模\数据\数据标准化处理.m (6598, 2004-03-08)
NN_RBF\BP-RBF-建模\数据\数据标准化处理（建模）.m (6609, 2003-12-21)
NN_RBF\BP-RBF-建模\数据\数据标准化处理（测试）.m (1816, 2003-12-21)
NN_RBF\BP-RBF-建模\数据\校验数据集.m (906, 2004-03-01)
NN_RBF\BP-RBF-建模\数据\预处理后的数据.m (5025, 2004-03-01)
NN_RBF\BP-RBF-建模\校验数据集.m (2089, 2003-12-20)
NN_RBF\BP-RBF-建模\线性回归法.m (8954, 2003-12-20)
NN_RBF\bp_ga\cross_over.m (680, 2005-09-16)
NN_RBF\bp_ga\fitness.m (1152, 2005-09-20)
NN_RBF\bp_ga\main.m (1866, 2005-09-22)
NN_RBF\bp_ga\mutation.m (546, 2005-09-22)
NN_RBF\bp_ga\select.m (114, 2005-08-02)
NN_RBF\bp算法\gabpEval.m (323, 2003-08-18)
NN_RBF\bp算法\gadecod.m (691, 2003-08-18)
NN_RBF\bp算法\nninit.m (352, 2003-08-17)
... ...

Matlab Routines for Subset Selection and Ridge Regression in Linear Neural Networks Version 1.013 February 25, 1997 The subdirectories below contain Matlab code and a postscript manual document for various algorithms to do with supervised learning using linear networks, particularly radial basis function networks. There is also Matlab code to re-run the examples given in the manual. If you start Matlab in this directory (so it reads ./startup.m) you will be able to run the example programs, e.g. forwardSelectExampl.m globalRidgeExample.m localRidgeExample.m. You may wish to incorporate parts of ./startup.m in your main startup.m file in $MATLABPATH (see the comments in ./startup.m). A brief description of the contents of each subdirectory follows. For further details see the ReadMe files in the subdirectories. Manual/ The postscript document manual.ps. RadialBasisFunctions/ rbfDesign.m for creating the design matrices of radial basis function networks given the basis function type, the centre positions, the centre sizes and the training set inputs. RidgeRegression/ globalRidge.m for optimising a single regularisation parameter given a design matrix and the training outputs. The example program globalRidgeExample.m. localRidge.m for optimising multiple regularisation parameters given a design matrix and the training outputs. The example program localRidgeExample.m. predictError.m for calculating various types of errors (e.g. GCV) given a design, the training outputs and a choice for the regularisation parameter(s). The example program predictErrorExample.m. SubsetSelection/ forwardSelect.m for forward selecting a subset given a selection set design, the training outputs and various options for controlling the search and regularisation. The example program forwardSelectExampl.m. Tutorial/ introExample.m to run the example given in the Tutorial Introduction section of the manual. Utilities/ Some useful little subroutines. Archive/ Version history. ----------------------------------------------------------------- Mark J L Orr, Centre for Cognitive Science, Edinburgh University mark@cns.ed.ac.uk http://www.cns.ed.ac.uk/people/mark.html

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：