Description: 用BP神经网络程序模拟销售预测，能对销售数据进行时间序列预测，采用VC实现-BP neural network simulation sales forecasts, sales data can be right for time series prediction, using VC Platform: |
Size: 220981 |
Author:杜昭翼 |
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Description: 用BP神经网络程序模拟销售预测，能对销售数据进行时间序列预测，采用VC实现-BP neural network simulation sales forecasts, sales data can be right for time series prediction, using VC Platform: |
Size: 221184 |
Author:杜昭翼 |
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Description: 用Matlab人工神经网络工具箱预测公交客流量，数据可以输入成文本形式，改变数据和精度即可使用。-Using Matlab neural network toolbox prediction bus traffic, data can enter into text form and change the data and the accuracy can be used. Platform: |
Size: 1024 |
Author:jsd |
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Description: 基于BP神经网络的方法建立预测模型，利用一组数据进行学习，预测将来-Based on BP neural network prediction model was established using a set of data to study, to predict the future Platform: |
Size: 13312 |
Author:liutao |
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Description: 利用主成分分析法对BP神经网络的输入参数进行降维，然后进行网络的训练，PCA-BP处理的结果同单一的bp相比，不仅提高了网络的收敛速度，而且提高了网络对预测数据分类的精度-Using principal component analysis method of BP neural network for dimensionality reduction of input parameters, and then training the network, PCA-BP deal with the results of a single bp, compared with not only improve the network convergence rate, and improve the network prediction data Classification accuracy Platform: |
Size: 1024 |
Author:娜娜 |
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Description: Neural Network Based Clustering
using Self Organizing Map (SOM) in Excel
Here is a small tool in Excel using which you can find clusters in your data set. The tool uses Self Organizing Maps (SOM) - originally proposed by T.Kohonen as the method for clustering.
* Neural Network based Clustering tool in Excel (209 KB in Zipped format. 947 KB when unzipped.)
Inside the downloaded zip file, you will find the Excel file containing the application. Before running it, I suggest that you go through the ReadMe worksheet. It contains brief instructions on how to run the tool.
If you are interested in building Prediction and Classification models in Excel using Feedforward-Backpropagation Neural Network, here are two small Excel based tools for you. Also, if you are interested in Tree based Classification models, here is a Tree based classifier in Excel.
-Neural Network Based Clustering
using Self Organizing Map (SOM) in Excel
Here is a small tool in Excel using which you can find clusters in your data set. The tool uses Self Organizing Maps (SOM)- originally proposed by T.Kohonen as the method for clustering.
* Neural Network based Clustering tool in Excel (209 KB in Zipped format. 947 KB when unzipped.)
Inside the downloaded zip file, you will find the Excel file containing the application. Before running it, I suggest that you go through the ReadMe worksheet. It contains brief instructions on how to run the tool.
If you are interested in building Prediction and Classification models in Excel using Feedforward-Backpropagation Neural Network, here are two small Excel based tools for you. Also, if you are interested in Tree based Classification models, here is a Tree based classifier in Excel.
Platform: |
Size: 214016 |
Author:Jessie |
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Description: 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 and output interfaces, that is, training data input, each layer weights and node thresholds output 2, the network' s learning, including prior to the spread of computing and the anti- to the spread of computing, error estimation, the right to modify the threshold value 3, the realization of the network prediction and so on. Which the network learning algorithm using variable step size and processing optimization algorithm with momentum term, after my experiment, network efficiency has greatly improved Platform: |
Size: 254976 |
Author:dcw |
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Description: 销售预测系统，可以根据历史销售数据对未来的销售量进行有效预测，采用BP神经网络对预测模型进行训练，可以达到不错效果
-Sales forecasting system, can be based on historical sales data on sales to predict the future, using BP neural network prediction model for training, can achieve good Xiao Guo Platform: |
Size: 1290240 |
Author:思思 |
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Description: Multi-step-prediction of chaotic time series based
on co-evolutionary recurrent neural network
协同进化递归神经网络的多步混沌时间序列预测-This paper proposes a co-evolutionary recurrent neural network (CERNN) for the multi-step-prediction of chaotic
time series, it estimates the proper parameters of phase space reconstruction and optimizes the structure of recurrent
neural networks by co-evolutionary strategy. The searching space was separated into two subspaces and the individuals
are trained in a parallel computational procedure. It can dynamically combine the embedding method with the capability
of recurrent neural network to incorporate past experience due to internal recurrence. The eff ectiveness of CERNN is
evaluated by using three benchmark chaotic time series data sets: the Lorenz series, Mackey–Glass series and real-world
sun spot series. The simulation results show that CERNN improves the performances of multi-step-prediction of chaotic
time series. Platform: |
Size: 152576 |
Author: |
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Description: 针对神经网络的瓦斯预测模型存在的泛化性能差且存在易陷入局部最优的缺点，提出了
基于最小二乘支持向量机(LS-SVM)时间序列瓦斯预测方法．由于标准最小二乘支持向量机
(L孓SVM)要求样本误差分布服从高斯分布，且标准LS-SVM丧失鲁棒性与稀疏性等特点，提出
了基于加权LS-SVM的瓦斯时间序列预测的方法，从而提高了标准L孓SVM模型的鲁棒性．其
中时间序列的嵌入维数与延迟时间采用了微熵率最小原则进行选取，在此基础上给出了基于加
权L孓SVM实现多步时间序列预测的算法实现步骤．最后利用MATLAB 7．1对其进行仿真研
究，通过鹤壁十矿1个突出工作面的瓦斯涌出数据实例对模型进行了验证．结果表明，加权
SVM模型比标准的L§SVM明显提高了鲁棒性，可较好地实现时间序列数据的多步预测．-The neural network gas prediction model is poor in generalization performance and
easy in fafling into the local optimal value．In order to overcome these shortcomings，we pro—
pose the time series gas prediction method of least squares support vector machine(L§SVM)．
However，in the LS-SVM case，the sparseness and robustness may lose，and the estimation of
the support values iS optimal only in the case of a Gaussian distribution of the error variables．
So，this paper proposes the weighted L孓SVM tO overcome these tWO drawbacks．Meanwhile，
the optimal embedding dimension and delay time of time series are obtained by the smallest dif—
ferential entropy method．On this basis，multi-step time series prediction algorithm steps are
given based on the weighted LS-SVM．Finally，the data of gas outburst in working face of Hebi
lOth mine iS adopted to validate this model．The results show that the predict effect of shortterm
the face gas emission is better using the weighted LS-SVM model than using Platform: |
Size: 490496 |
Author:wanggen |
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Description: 降水短期气候预测是一个非常复杂、重要的研究课题。为了提高其预测能力，拟采用1959—2011 年逐月74 项大气环流特征量序列、月平均500 hPa 高度场和月平均海温场，选取预测因子；用主分量分析方法提取样本数据中主要信息为综合因子。用粒子群优化人工神经网络方法，建立宣城市夏季降水短期气候预测模型。对2007—2011 年宣城市夏季降水预报检验结果表明，粒子群优化人工神经网络收敛速度快，迭代次数少；试报平均绝对误差是66.5 mm，绝对值平均相对误差10.5 ，预测精度高，具有很好的应用推广前景。 -Precipitation of short-term climate prediction is a very complex and important research topic. Intends to adopt in order to improve its ability to predict the the 1959-2011 monthly 74 atmospheric circulation feature series, monthly mean 500 hPa height field and monthly average sea surface temperature field, select the predictor extract the sample data using principal component analysis for the Synthesis factor. Artificial neural network using particle swarm optimization method, Xuancheng City in summer rainfall in short-term climate prediction model. 2007-2011 declared the city in summer precipitation forecast verification results show that the particle swarm optimization artificial neural network convergence speed, fewer iterations trial reported an average absolute error is 66.5 mm, the absolute value of the average relative error of 10.5 , high prediction accuracy, good application prospect. Platform: |
Size: 1594368 |
Author:mali |
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Description: 在工程应用中经常会遇到一些复杂的非线性系统, 这些系统状态方程复杂, 难以用数学方法准确建模。在这种情况下, 可以建立B P神经网络表达这些非线性系统。该方法把未知系统看成是一个黑箱, 首先用系统输入输出数据训练B P神经网络, 使网络能够表达该未知函数, 然后就可以用训练好的B P神经网络预测系统输出。-In engineering applications often encounter a number of complex nonlinear systems, complex equation of state of these systems is difficult to accurately modeled using mathematical methods. In this case, BP neural network can be established express these nonlinear systems. The method of the unknown system as a black box, first with the system input and output data to train BP neural network so that the network can express the unknown function, then you can use the trained BP neural network prediction system output. Platform: |
Size: 52224 |
Author:吴江 |
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Description: 神经网络训练拟合根据寻优函数的特点构建合适的BP神经网络,用非线性函数的输入输出数据训练BP神经网络,训练后的BP神经网络就可以预测函数输出。遗传算法极值寻优
把训练后的BP神经网络预测结果作为个体适应度值,通过选择、交叉和变异操作寻找函数的
全局最优值及对应输入值。
-Neural network training function fitting based optimization features built right on BP neural network, using non-linear function of the input output data trained BP neural network, the trained BP neural network can predict the function output. The genetic algorithm optimization extreme training BP neural network prediction results as the individual fitness value through selection, crossover and mutation find the global optimal value function and the corresponding input value. Platform: |
Size: 3072 |
Author:吴军 |
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Description: 本课题首先根据寻优函数的特点构建合适的BP神经网络,用非线性函数的输入输出数据训练BP神经网络,训练后的BP神经网络就可以预测函数输出。遗传算法极值寻优
把训练后的BP神经网络预测结果作为个体适应度值,通过选择、交叉和变异操作寻找函数的
全局最优值及对应输入值。
-Neural network training function fitting based optimization features built right on BP neural network, using non-linear function of the input output data trained BP neural network, the trained BP neural network can predict the function output. The genetic algorithm optimization extreme training BP neural network prediction results as the individual fitness value through selection, crossover and mutation find the global optimal value function and the corresponding input value. Platform: |
Size: 4096 |
Author:吴军 |
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Description: LVQ神经网络的预测——人脸朝向识别的源程序及数据。用matlab程序编写。-LVQ neural network prediction- face towards the identification of the source code and data. Using matlab programming. Platform: |
Size: 5130240 |
Author:木子 |
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Description: 基于广义回归神经网络的数据预测，使用交叉验证的GRNN神经网络预测程序，包含BP和GRNN效果比较程序。两网络用相同的数据进行训练。-Based on generalized regression neural network data prediction, using cross-validation GRNN neural network prediction program, including BP and GRNN effect comparison procedures. Two networks use the same data for training. Platform: |
Size: 8192 |
Author:rong |
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Description: 采用最先进的殖民竞争算法Imperialist competition algorithm优化BP神经网络的初始权值、阈值，进行风电功率预测，带数据和实例，ica为主程序-Using the most advanced colonial competitive algorithm Imperialist competition algorithm to optimize the initial weights of BP neural network, threshold, carry wind power prediction with data and examples, ica-based program Platform: |
Size: 17408 |
Author:Victoria |
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Description: 以某光伏电站实测数据为比较对象用拟牛顿法小波神经网络建立光伏发电系统短期功率预测模型。-Based on the measured data of a PV power plant, the short- term power prediction model of photovoltaic power generation system is established by using quasi- Newton method wavelet neural network. Platform: |
Size: 894976 |
Author:HZQ |
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Search - data prediction using neural network