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kongming
- 神经网络训练根据Kolmogorov定理，输入层有14个节点，所以中间层有29个节点 ％中间层神经元的传递函数为 tansig ％输出层有8个节点，其神经元传递函数为logsig ％训练函数采用traingdx-neural network training under the Kolmogorov theorem, input layer has 14 nodes, Therefore, the intermediate
rbfn
- 利用MATLAB对神经网络进行编程,用newff()创建两层前向网络。网络输入范围[-1 1]，第一层有10个tansig神经元-using MATLAB right neural network programming with newff () to the creation of a two-tier network. Network input range [-1 1], the first layer 10 tansig ne
rbfn
- 利用MATLAB对神经网络进行编程,用newff()创建两层前向网络。网络输入范围[-1 1]，第一层有10个tansig神经元-using MATLAB right neural network programming with newff () to the creation of a two-tier network. Network input range [-1 1], the first layer 10 tansig ne
kongming
- 神经网络训练根据Kolmogorov定理，输入层有14个节点，所以中间层有29个节点 ％中间层神经元的传递函数为 tansig ％输出层有8个节点，其神经元传递函数为logsig ％训练函数采用traingdx-neural network training under the Kolmogorov theorem, input layer has 14 nodes, Therefore, the intermediate
invertedpendulum
- 倒立摆是一种复杂、时变、非线性、强耦合、自然不稳定的高阶系统，许多抽象的控制理论概念都可以通过倒立摆实验直观的表现出来。基于人工神经网络BP算法的倒立摆小车实验仿真训练模型，其倒立摆BP网络为4输入3层结构。输入层分别为小车的位移和速度、摆杆偏离铅垂线的角度和角速度。隐含层单元数16个。输出层设置为1个输出单元。输入层采用Tansig函数，隐含层采用Logsig函数，输出层采用Purelin函数。用Matlab 6.5数值计算软件对模型
BPNN4_2
- load training.txt load TrainOut.txt load validation.txt load ValOut.txt load testing.txt load TestOut.txt INPUT=[training validation testing] OUTPUT=[TrainOut ValOut TestOut] net=newff(INPUT,OUTPUT,200,{ t
RLS+MatriXReseting+ForgetFactor
- this matlab code for estimating the static linear system(system function is time variable) with Recursive Least Squre and 2 solutions for better result. 1- using the Covariance Matrix Reseting in a specefic time. 2-u
KalmanFilter
- this matlab code for estimating the static linear system(system function is time variable) with Kalman Filter. this program is written by matlab 7.0. Here we want to estimate the below function: this is matlab code
program
- C++实现神经网络，包括三个函数，sigmoid，tanh，tansig的实现方式。-designed by C++，implication neural network，include sigmoid，tanh，tansig
BPNNPID
- 神经网络的C++源程序。提供sigmod，tanh，tansig三种函数的实现方式。-Neural Networks C++ source code. Provide sigmod, tanh, tansig three functions are implemented.
microcontroller_neural_network
- This a simple program to calculate the output of artificial neural network (ANN) using microcontroller ATMega 32. Assume that the neural architecture is : 2 hidden layers with 4 and 2 neurons respectively and 1 laye
Function-approximation
- 函数逼近的MATLAB程序，本程序设计一个两层的bp网络用于函数逼近，隐层的激活函数为 tansig,输出层激活函数为purelin线性函数 -Function approximation of the MATLAB program, the program design of a two-tier network for function approximation bp, hidden layer activation fun
code
- 三层BP神经网络对药品的销售进行预测。：输入层有四个结点，隐含层结点数为5，隐含层的激活函数为tansig；输出层结点数为1个，输出层的激活函数为logsig，并利用此网络对药品的销售量进行预测，预测方法采用滚动预测方式，即用前四个月的销售量来预测第四个月的销售量。-Three-layer BP neural network to forecast the sales of drugs. : Input layer has four n
shenjingwangluo
- T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm )
gm11
- function exp85 clear all p=[0:0.1:1.1] t=[22.4570 26.6012 12.6416 5.9367 6.9265 28.2432 31.5068 37.0166 7.8947 1.0398 12.7095] net=newff([0 1],[5 1],{ tansig purelin }, traingdx , learngdm ) net.trai
twodimapproximationbp
- 单输出函数Y=SIN(X)逼近问题的bp程序:假设网络结构为3--2--1，输入维数M,共N个样本,一般输入不算层，输出算层- 激活函数: hardlim---(0,1),hardlims---（-1,1),purelin,logsig---(0,1),tansig----(-1,1) softmax,poslin,radbas,satlin,satlins,tribas 训练算法: 1.traing
SingularValueDecomposition
- 人脸识别过程中的奇异值分解算法代码，亲测可用，实现步骤为： feature = allFeature(1) //featurenumber=8,16,24,32,48,64,80 [pn,pnewn,t,num_train,num_test] = train_test(feature,num_train) //num_train=1~10 [net] = createBP(pn) //110,tansig,pur