搜索资源列表
Project1
- 多层神经网络,在训练过程中采用自适应学习率Adagrad方法。可以实现回归或分类问题。(The adaptive learning rate Adagrad method is adopted in the training process of the multilayer neural network. Regression or classification problems can be achieved.)
27796710BPtrain
- 基于BP神经网络的全国人口预测本文以1970-2013年的中国人口统计数据为依据,论证BP网络预测模型的可行性同时给出了利用MATLAB神经网络工具箱设计BP网络的方法和步骤。利用样本数据对网络进行训练,并根据训练后的网络对未来人口数量作出预测。仿真结果表明该方法实际可行并对2014—2017的全国人口数量进行预测。。(National Population Prediction Based on BP neural network)
BP NN jiaochayanzheng
- 基于改进的BP神经网络的算法,并结合了交叉验证算法,划分为训练集与测试集进行改进。(Based on the improved BP neural network algorithm and the cross validation algorithm, the training set and test set are improved.)
Untitled
- 建立BP神经网络, 12个隐层神经元,4个输出神经元 tranferFcn属性 'logsig' 隐层采用Sigmoid传输函数 tranferFcn属性 'logsig' 输出层采用Sigmoid传输函数 trainFcn属性 'traingdx' 自适应调整学习速率附加动量因子梯度下降反向传播算法训练函数 learn属性 'learngdm' 附加动量因子的梯度下降学习函数(The BP neural network is established, with 12 hidden la
RBF
- RBF神经网络应用数据预测,有训练集,测试集(Application of BF neural network data forecas)
libsvm-3.17
- 为了真实有效地提取网络安全态势要素信息,提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化态势要素获取框架中,根据Agent节点功能的不同,划分为不同的层次。利用主成分分析(Principal Component Analysis, PCA)对训练样本属性进行约简并对特殊属性编码融合处理,按照处理结果改进概率神经网络(Probabilistic Neural Network, PNN)结构,以降低系统复杂度。然后以改进的PNN作为基分类器,结合自适应增强算法,通过基分类器反
src-BP
- BP算法的神经网络的源代码, 可以根据向量建立网络,网络的训练结果和初始结构可以用XML保存和载入。 其中 Compressor/TrainerWithDiagram.class , 是一个用于演示的训练器, 产生制定范围内的数,生成随即样本, 并训练。 MainClass.class , 指一个数据压缩器的启动界面。(BP algorithm of neural network source code, you can build a network according to the ve
character recognition
- CNN卷积神经网络对字符进行训练、字符识别相关代码(Printed ink jet character recognition)
bp
- 本代码是一份bp神经网络代码,通过训练、检验、预测,可以实现对网络的完美应用,有需要的同学可以看看。(This code is a BP neural network code, through training, testing, prediction, you can achieve the perfect application of the network, there is a need for students to see.)
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- BP神经网络matlab完整代码,包括训练代码、测试代码(BP neural network matlab complete code, including the training code, test code, the image directly into the network is trained to)
DNRBM
- 采用栈式 RBM 的深度神经网络。自训练, 自控制(This paper is a continuation of the depth of the neural network and the choice of the previous predictor. Here we will be covered by the depth of the neural network characteristic of stacked RBM initialization, and it is in
bp-分类器
- 这是bp神经网络的M文件,包括BP网络的第一阶段学习期(训练加权系数wki,wij),BP网络的第二阶段工作期(根据局训练好的wki,wij和给定的输入计算输出),程序里有详细注释。该程序被用来作为分类器使用。(This is the BP neural network M files, including the first phase of BP network learning period (training weighting coefficient wki, wij), the se
2-GRNN_PNN_work
- GRNN和PNN神经网络对数据的拟合、训练和二者对比(Data training of GRNN and PNN and comparison of the two)
3-Competitive neural network
- 竞争神经网络的matlab实现,对一组数据进行拟合和训练,得出精度(The matlab implementation of the competitive neural network, fitting and training a set of data to get the accuracy)
BPNN_Python-master
- BP神经网络一个简单的异或回归训练,适合入门。(BP neural network A simple XOR regression training, suitable for getting started.)
BP
- bp神经网络进行交通预测的Matlab源代码 数据为1986年到2000年的交通量 ,网络为3输入,1输出 15组数据,其中9组为正常训练数据,3组为变量数据,3组为测试数据(Matlab source code for traffic prediction by BP neural network The data is the traffic volume from 1986 to 2000. The network is 3 input and 1 output. 15 group
FeatureExtractionUsingAlexNetExample
- 本示例展示了怎样从一个预处理的卷积神经网络中提取特征,并用这些特征去训练一个图像分类器。(This example shows how to extract learned features from a pretrained convolutional neural network, and use those features to train an image classifier. Feature extraction is the easiest and fastest way use
BP,RBF
- BP神经网络作为一种前馈性的神经网络,RBF神经网络由于其独特的联想记忆功能,常常用来用于识别和优化计算方问题上。分别对这两种算法用于对逼近非线性函数进行编程,观察其拟合情况后,用其他未训练的样本数据进行泛化能力分析。(BP neural network is a feed-forward neural network. RBF neural network is often used to identify and optimize the computation problem due to
image
- cifar 卷积神经网络 通过cnn识别图片,对神经网络进行训练,在识别cifar库(convolutional neural network)
BP 神经网络
- BP(back propagation)神经网络是1986年由Rumelhart和McClelland为首的科学家提出的概念,是一种按照误差逆向传播算法训练的多层前馈神经网络,是目前应用最广泛的神经网络。(BP (back propagation) neural network is a concept put forward by scientists headed by Rumelhart and McClelland in 1986. It is a multilayer feedforw