Description: 该程序专门用于检验bp神经网络的连接权值-devoted to the testing procedures bp neural networks and the connection weights Platform: |
Size: 1024 |
Author:张凌 |
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Description: Program for training and testing the Elman Recurrent Neural Network model when used to predict Platform: |
Size: 1024 |
Author:Abd Kadir |
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Description: 基于BP神经网络，根据鸢尾花多组数据先训练网络再对样本进行测试，给出分类结果-Based on BP neural network, in accordance with multiple sets of data iris network before training again for testing classification results are given Platform: |
Size: 4096 |
Author:wawa |
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Description: 一个自己编写的三层BP神经网络，通过测试，初学者可以参考一下-I have written a three-layer BP neural network, testing and reference for beginners Platform: |
Size: 1024 |
Author:jiangjinshou |
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Description: RNNSIM ver. 1.0 is a program with an intercative graphical user interface
(GUI) that runs under MATLAB ver. 5.0 or higher. The program can be used
in training and testing the Random Neural Network(RNN) models.
This version (ver. 1.0) implements only the 3 layer feed forward RNN model.
In the next versions, the multi hidden layers and the recurrent RNN models
can be implemented. To obtain faster training, the training section can be
written as a MEX file and invoked from the GUI.
If you have the m files in the directory rnnsim for example, then you can
run the program following the next steps:
1- run MATLAB as usual
2- from the MATLAB command window, write cd rnnsim
3- from the MATLAB command window, write rnnsim- RNNSIM ver. 1.0 is a program with an intercative graphical user interface
(GUI) that runs under MATLAB ver. 5.0 or higher. The program can be used
in training and testing the Random Neural Network(RNN) models.
This version (ver. 1.0) implements only the 3 layer feed forward RNN model.
In the next versions, the multi hidden layers and the recurrent RNN models
can be implemented. To obtain faster training, the training section can be
written as a MEX file and invoked from the GUI.
If you have the m files in the directory rnnsim for example, then you can
run the program following the next steps:
1- run MATLAB as usual
2- from the MATLAB command window, write cd rnnsim
3- from the MATLAB command window, write rnnsim
Platform: |
Size: 63488 |
Author:hacen |
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Description: 本代码是采用BP神经网络 对上海港的集装箱吞吐量进行预测，内涵两个程序包，一个为训练网络，里一个是测试之用，请参考，希望有所帮助-This code is used BP neural network to predict the Shanghai Port' s container throughput, meaning the two packages, one for training the network, where one is testing purposes, please refer to the hope that help Platform: |
Size: 1024 |
Author:刘杨 |
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Description: 此程序是神经网络中径向基函数的OLS算法，在MATLAB中实现。用一个2－n－1结构的RBF网对SISO系统进行建模，网络的两个输入为u(k-1)和y(k-1)，输出为 y(k)。令y(0)=0，按飞线性系统产生200个样本，其中前100个样本用于训练，后100个样本用于测试。-This program is the radial basis function neural network of OLS algorithm is implemented in MATLAB. With a 2-n-1 structure of RBF network model of the SISO system, the network of two input u (k-1) and y (k-1), output y (k). So that y (0) = 0, the linear system produced by flying 200 samples, of which the first 100 samples for training, after 100 samples for testing. Platform: |
Size: 1024 |
Author:lingyun |
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Description: The early detection of arrhythmia is very important
for the cardiac patients. This done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data.-The early detection of arrhythmia is very important
for the cardiac patients. This is done by analyzing the
electrocardiogram (ECG) signals and extracting some features
from them. These features can be used in the classification of
different types of arrhythmias. In this paper, we present three
different algorithms of features extraction: Fourier transform
(FFT), Autoregressive modeling (AR), and Principal Component
Analysis (PCA). The used classifier will be Artificial Neural
Networks (ANN). We observed that the system that depends on
the PCA features give the highest accuracy. The proposed
techniques deal with the whole 3 second intervals of the training
and testing data. We reached the accuracy of 92.7083
compared to 84.4 for the reference that work on a similar
data. Platform: |
Size: 273408 |
Author:Amit Majumder |
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Description: Image classification using Backpropragation Neural Network with data training set and data testing Platform: |
Size: 2433024 |
Author:Apraz |
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Description: 采用MATLAB神经网络中的BP网络进行26个英语字母的仿真识别。采用了改进型的BP算法设计了26个字符的模式识别系统。并对该系统进行了训练和测试。-MATLAB neural network in the BP network for 26 English letters of the simulation. The pattern recognition system based on the improved BP algorithm is designed. And training and testing of the system. Platform: |
Size: 1024 |
Author:shinfer |
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Description: 采用malab的elman神经网络训练和测试294期蓝球数据预测下一期蓝球号码。-The elman using matlab neural network training and testing 294 basketball data to predict the next number. Platform: |
Size: 2048 |
Author:毅宜 |
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Description: Neural network programming，包括matlab和C#的实现源代码，包括训练数据和测试数据-Neural network programming, including matlab and C# source code to achieve, including the training and testing data Platform: |
Size: 84992 |
Author:hb |
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Description: 深度神经网络在测试时面对如此大的网络是很难克服过拟合问题的。
Dropout能够很好地解决这个问题。通过阻止特征检测器的共同作用来提高神经网络的性能。这种方法的关键步骤在于训练时随机丢失网络的节点单元包括与之连接的网络权值。在训练的时候，Dropout方法可以使得网络变得更为简单紧凑。在测试阶段，通过Dropout训练得到的网络能够更准确地预测网络的输出。这种方式有效的减少了网络的过拟合问题，并且比其他正则化的方法有了更明显的提升。
本文通过一个简单的实验来比较使用Dropout方法前后网络的性能优劣情况。(It is difficult to overcome the problem of fitting a deep neural network in the face of such a large network in testing.
Dropout can solve this problem well. The performance of the neural network is improved by preventing the common action of the feature detector. The key step of this method is that the node unit of the random loss network consists of the network weights connected to it during the training. In training, the Dropout method can make the network more compact. In the test phase, the network trained by Dropout can predict the output of the network more accurately. This method effectively reduces the over fitting problem of the network, and has a more obvious improvement than the other regularization methods.
In this paper, a simple experiment is used to compare the performance and performance of the Dropout method.) Platform: |
Size: 311296 |
Author:转角的狐狸 |
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Description: 用C语言编程实现神经网络的构建和训练的程序代码
以及matlab编程实现偏最小二乘法(Programming and training program code in C language;Matlab programming for partial least square method) Platform: |
Size: 1510400 |
Author:qingman |
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Description: 采用三层BP神经网络结构，输入层神经元数为5，隐含层神经元数为3，输出层神经元数为1，使用MATLAB编写。
将所给数据按14：1分为训练样本集，和测试样本集，经测试及分析，预测误差为0.1700，误差较小。
网络训练好后，输入前一天的6组数据，即：最高价、最低价、开盘价、收盘价、成交量，就能自动预测出后一天的收盘价。(The structure of three-layer BP neural network is adopted. The number of neurons in the input layer is 5, the number of neurons in the hidden layer is 3, and the number of neurons in the output layer is 1.
The data are divided into training sample set and testing sample set according to 14:1. After testing and analysis, the prediction error is 0.1700 and the error is small.
After network training, input six sets of data from the previous day, namely, the highest price, the lowest price, the opening price, the closing price and the volume, and then automatically predict the closing price of the next day.) Platform: |
Size: 306176 |
Author:nz106 |
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Description: 该课题为基于MATLAB bp神经网络的手写汉字识别系统。可以利用鼠标手写中文汉字进行训练，测试，可以识别任何字体，只需要到GUI界面面板更换即可。在GUI界面就可以随写随训练中文，不需要到后台手动更换文字训练，方便快捷上档次。(This project is a handwritten Chinese character recognition system based on Matlab bp neural network. You can use the mouse to write Chinese characters for training and testing. You can recognize any font. You just need to change it on the GUI interface panel. In the GUI interface, you can train Chinese as you write. You don't need to manually change the text training in the background. It is convenient and fast to upgrade the level.) Platform: |
Size: 332800 |
Author:可乐一生 |
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Search - matlab neural training testing