Description: 程序名：ga_bp_predict.cpp
描述： 采用GA优化的BP神经网络程序，用于单因素时间
序列的预测,采用了单步与多步相结合预测
说明： 采用GA(浮点编码)优化NN的初始权值W[j][i],V[k][j]，然后再采用BP算法
优化权值-Program name: ga_bp_predict.cpp Description: The GA-optimized BP neural network procedure for single-factor time series prediction using the single-step and multi-step prediction combining Description: using GA (floating point coding) to optimize the initial NN weights W [j] [i], V [k] [j], then BP algorithm to optimize the use of weights Platform: |
Size: 6144 |
Author:fk774 |
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Description: c++做的小波分析程序，用小波神经网络来对时间序列进行预测。-c++ to do the wavelet analysis procedures, using wavelet neural network to time series prediction. Platform: |
Size: 6144 |
Author:李建伟 |
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Description: 资源分配神经网络解决Mackey-Glass时间序列预测函数逼近问题-Neural network to solve the allocation of resources Mackey-Glass time series prediction function approximation problem Platform: |
Size: 1024 |
Author:吴强 |
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Description: 利用神经网络预测时间序列，以太阳黑子和实际的转子故障信号为例，matlab编程-Using neural network time series prediction to sunspots and the actual rotor fault signal as an example, matlab programming Platform: |
Size: 195584 |
Author:苏文胜 |
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Description: matlab格式源代码。功能：径向基神经网络算法源码和应用于时间序列模型建立和预测问题。-matlab source code format. Function: RBF neural network algorithm source code and applies to time-series model and prediction of the problem. Platform: |
Size: 5120 |
Author:magic |
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Description: matlab编写的基于混沌时间序列的神经网络预测，包括一步和多步预测算法。-matlab prepared chaotic time series based on the neural network to predict, including step and multi-step prediction algorithm. Platform: |
Size: 9216 |
Author:jcuaon |
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Description: MATLAB implementation of time series prediction Based on the VQTAM method described in the following papers:
G. A. Barreto & A. F. R. Araujo (2004)
"Identification and Control of Dynamical Systems Using the Self-Organizing Map"
IEEE Transactions on Neural Networks, vol. 15, no. 5. Platform: |
Size: 23552 |
Author:Carlos Wilson |
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Description: Elman递归神经网络对时间序列的预测代码，做的效果还行，仅供参考-Elman recurrent neural network for time series prediction code, do the results were OK for reference purposes only Platform: |
Size: 30720 |
Author:chenshengli |
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Description: 为了选择神经网络的最好结构以及增强模型的推广能力，提出一种自适应支持向量回归神经网络(SVR—NN)。SVR—NN 用支持向量回归(SVR)方法获得网络的初始结构和权值， 白适应地生
成网络隐层结点，然后用基于退火过程的鲁棒学习算法更新网络结点疹教和权 主。 SVR—NN有很
好的收敛性和鲁棒性，能抑制由于数据异常和参数选择不当所导致的“过拟合，’现象。将SVR—NN
应用到时间序列预测上。结果表明，SVR．NN预测模型能精确地预测混沌时间序列，具有很好的
理论和应用价值。-Abstract：To select the‘best’structure of the neural networks and enhance the generalization ability of models．a support
vector regression neural networks fSVR-NN)was proposed．Firstly，support vector regression approach was applied to
determine initial structure and initial weights of SVR．NN SO that the number of hidden layer nodes can be constructed
adaptively based on support vectors．Furthermore，an annealing robust learning algorithm was further presented to fine
tune the hidden node parameters and weights of SVR一ⅣM The adaptive SVR．NN has faSt convergence speed and robust
capability．and it can also suppress the ‘orerfitting’phenomena when the train data ncludes outliers．The adaptive
SVR．NN was then applied to time series prediction．Experimental results show that the adaptive SVR．ⅣⅣ can accurately
predict chaotic time series，and it iS valuable in both theory and application aspects． Platform: |
Size: 316416 |
Author:11 |
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Description: Neural Network OCR.
There are many different approaches to optical character recognition problem. One of the most common and popular approaches is based on neural networks, which can be applied to different tasks, such as pattern recognition, time series prediction, function approximation, clustering, etc.
In this article, I ll try to review some approaches for optical character recognition using artificial neural networks. The attached project is aimed as a research project, so don t try to find here a ready solution for scanned document processing.
Platform: |
Size: 367616 |
Author:reyjav |
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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: 采用模糊神经网络anfis预测混沌时间序列的源程序。-The source program of Using fuzzy ANFIS neural network for predicting chaotic time series. Platform: |
Size: 571392 |
Author:胡玉霞 |
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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: 一篇经典的学术论文，基于小波神经网络的时间序列预测，适合初学者学习-The Application of Wavelet Neural Network in Time Series Prediction and Platform: |
Size: 471040 |
Author:huangxin |
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Search - time series prediction neural