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BP神经网络源程序
- 基于C开发的三个隐层神经网络，输出权值、阈值文件，训练样本文件，提供如下函数：1）初始化权、阈值子程序；2）第m个学习样本输入子程序；3）第m个样本教师信号子程序；4）隐层各单元输入、输出值子程序；5）输出层各单元输入、输出值子程序；6）输出层至隐层的一般化误差子程序；7）隐层至输入层的一般化误差子程序；8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序；9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序；10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子程序；11）第一
CSNN
- This package contains 6 algorithms for training cost-sensitive neural networks. They are over-sampling, under-sampling, threshold-moving, SMOTE and two ensemble methods, i.e. hard-ensemble and soft-ensemble.
Delta
- 神经网络感知器学习规则：Delta学习规则。采用Delta学习规则，进行权值调整，阈值变换函数采用双极性连续函数。程序简单易懂，希望对大家有所帮助！-Neural network perceptron learning rule: Delta learning rule. Delta learning rule used to carry out the right to adjust the value of the threshold bipolar transfer function us
License_Plate_NN_OCR
- Neural Network Based Threshold Determination for Malaysia License Plate Character Recognition
ga_bp
- ga优化神经网权值&阈值程序。为了方便，我把几个matlab的程序都放在txt文件里，使用时，对照把txt内容复制进matlab即可-ga optimization neural network weights & threshold procedures. For convenience, I have several matlab programs on the txt file, use, control the content of the txt you copied into
GaBp
- 用GA训练BP网络的权值、阈值从而优化神经网络-GA training BP network with the right value, the threshold in order to optimize the neural network
gabp
- ga优化神经网权值&阈值程序。 优化的基本原理和过程很多论文可以查到，在此不必赘述我就把用gaot5的小程序贴在下面吧，也是y=1/x-ga optimization neural network weights & threshold procedures. Optimization of the basic principles and processes can be found in many papers, do not have to go into details he
GABPTOOLBOX
- 用遗传算法优化BP神经网络的权值及其阀值-Using genetic algorithm optimization BP neural network weights and threshold
bpnet
- 用MATLAB实现二层bp神经网络的计算。可以改变阈值和权值以改进算法，并可以将该方法推广到多层网络。-Using MATLAB to achieve the second floor bp neural network computing. Can change the threshold value and weight to improve the algorithm and the method can be extended to the multi-layer network.
BP
- 神经网络bp算法VC++实现网络的相关运算有：1、网络的输入输出接口，即训练数据的输入，各层权值和节点阈值的输出；2、网络的学习，包括前向传播运算和反向传播运算，误差估计，权值阈值修改；3、网络预测的实现等等。其中网络的学习算法采用变步长和加动量项的优化学习算法，经过我的实验对网络的学习效率有很大提高-Neural network bp algorithm VC++ to achieve the network-related operations: 1, the network input a
BP-matlab
- 基于C开发的三个隐层神经网络,包括 1）初始化权、阈值子程序； 2）第m个学习样本输入子程序； 3）第m个样本教师信号子程序； 4）隐层各单元输入、输出值子程序； 5）输出层各单元输入、输出值子程序； 6）输出层至隐层的一般化误差子程序； 7）隐层至输入层的一般化误差子程序； 8）输出层至第三隐层的权值调整、输出层阈值调整计算子程序； 9）第三隐层至第二隐层的权值调整、第三隐层阈值调整计算子程序； 10）第二隐层至第一隐层的权值调整、第二隐层阈值调整计算子
GA-BP
- 这是一个采用遗传算法优化bp神经网络权值阈值的MATLAB程序-This is a genetic algorithm to optimize neural network weights bp threshold MATLAB program
ga
- VS 2005下编写的遗传算法，采用面向对象的方式实现的遗传算法的思想，可用于神经网络的初始权值和阈值的优化。-VS 2005, prepared under the genetic algorithm, object-oriented approach to achieve the idea of genetic algorithms, neural networks can be used for the initial weights and threshold optimization.
test
- 关于BP神经网络的程序代码，包括阈值和权值的调整，现在在石油开发测井应用非常广泛-Regarding the BP neural network program code, include threshold value and the adjustment of oil well logging, now is used extensively
05363793
- An Improved PSO Algorithm to Optimize BP Neural Network Abstract This paper presents a new BP neural network algorithm which is based on an improved particle swarm optimization (PSO) algorithm. The improved PSO (which is called IPSO) algori
ga_bp
- 网络结构、初始连接权值和阈值的选择对网络训练的影响很大，但是又无法准确获得，针对这些特点可以采用遗传算法对神经网络进行优化-Network structure, initial connection weights and threshold selection a great impact on the network training, but it is impossible to accurately obtain, these features can be used for the
Neural-network-recognition
- 网络将输入模式加权求和、与门限比较、再进行非线性运算，得到网络的输出-The network will be weighted sum input mode, compared with the threshold, then a nonlinear operator to get the output of the network
bp-neural-network(3-hidden-layer)
- 3隐层的bp神经网络，有详细的注释，各隐层的权值调整、输出层阈值调整，学习样本输出层至隐层一般化误差-3 bp neural network hidden layer, there are detailed notes, each hidden layer weight adjustment, the output layer threshold adjustment, learning sample output layer to the hidden layer generalization
genetic--BP-weights-and-threshold
- 利用遗传算法优化BP神经网络权值和阈值的matlab-The use of genetic algorithm to optimize BP neural network weights and threshold
TrainingNeuralNetworkswDEA-XORproblem
- Training Neural Networks with Threshold Activation Functions and Constrained Integer Weights