BP模型逼近异或问题，可观测隐层节点数及隐层数对模型的影响...

矩阵计算库，经改编过的，可以扩充，可读性很好。-matrix calculation library, adapted too, can be expanded, good readability.

神经网络仿真工具，本程序是BP算法的演示程序, 其中的Levenberg-Marquardt算法具有实用价值.-Neural network simulation tool, this program is BP algorithm demo program in which the Levenberg-Marquardt algorithm has practical value.

支持向量机中的JAVA程序 初学者可以方便使用-Support Vector Machine in the JAVA program for beginners can easily use

数据挖掘算法 关联规则算法 apriori算法（c语言版本）-algorithm of data mining algorithm of corelationship rule algorithm of aproori (C language edition)

BP神经网络自适应步长训练算法，采用最小误差法，梯度下降法，自适应调节权值-BP neural network training anaysis, is realized by using error feed back, gradient descent applied updating of synaptic weights

Fast ICA，一种ICA的快速算法，有完整的论文（两种格式，pdf和ps的）-Fast ICA, the ICA a fast algorithm, a complete paper (both formats, pdf and ps)

智能交易系统，，根据均线信号自动买卖，自动化资金管理，推荐1H，GBP/USD，EUR/USD，NZD/USD-2 93 f: brainpower dealt system complete new twenty piece brainpower dealt system 20 piece brainpower dealt system [ea] 2bar trend_ron_mt4_v01.mq4 17:29 (-unexpected token 2 93 f: brainpower dealt system complete new twenty piece brainpower dealt system 20 piece brainpower dealt system [ea] 2bar trend_ron_mt4_v01.mq4 30:31 (-unexpected token 2 93 f: brainpower dealt system complete new twenty piece brainpower dealt system 20 piece brainpower dealt system [ea] 2bar trend_ron_mt4_v01.mq4 31:32 (-unexpected token 2 93 f: brainpower dealt system complete new twenty piece brainpower dealt system 20 piece brainpower dealt system [ea] 2bar trend_ron_mt4_v01.mq4 39:31 (-unexpected token 2 93 f: brainpower dealt system complete new twenty piece brainpower dealt system 20 piece brainpower dealt system [ea] 2bar trend_ron_mt4_v01.mq4 40:32 (-unexpected token

libview的libsvm程序，它的应用比较方便。-libview the libsvm procedure, its application more convenient.

一个神经网络小脑CMACmatlab算法例子- A nerve network cerebellum CMACmatlab algorithm example

调用过程 CM = Confusion_matrix(train_predicts, train_targets) [combining_predicts, errorrate] = combining_NB(DP, test_targets, CM) DP，三维数组，(i,j,k)为第k个样本的DP矩阵 targets 为 0 1 2 -process called CM = Confusion_matrix (train_predicts, train_targets) [combining_predicts, errorrate] = combining_NB (DP, test_targets, CM) DP, three-dimensional array (i, j, k) for the k samples of DP matrix targets for 0 1 2