Description: This book is about the use of artificial neural networks for supervised learning problems. Many such problems occur in practical applications of artificial neural networks. For example, a neural network might be used as a component of a face recognition system for a security appli-
cation. After seeing a number of images of legitimate users' faces, the network needs to determine accurately whether a new image corresponds to the face of a legitimate user or an imposter. In other applications, such as the prediction of future price of shares on the stock exchange, we may require a neural network to model the relationship between a pattern and a real-valued quantity. Platform: |
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Author:kj5566 |
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Description: 本文分析小波神经网络的特点重点研究在电力负荷预测中连续小波神经网络与BP神经网络相比具有的优缺点对认识和应用小波神经网络具有重要意义算例表明在网络结构相同的情况下连续小波神经网络比BP神经网络具有更高的预测精度-wavelet analysis of this neural network research focused on the characteristics of the power load forecasting continuous wavelet neural network and BP Neural networks have their advantages and disadvantages compared to the understanding and application of wavelet neural networks is of great significance example shows the network structure with the continuous wavelet neural network than BP neural network has a higher accuracy of prediction Platform: |
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Author:bluebowl |
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Description: 用概率神经网络预测股市的方向变化.对研究神经网络在金融市场上的应用很有帮助。-Using probabilistic neural network prediction of the direction of changes in the stock market. The study of neural networks in the financial market applications helpful. Platform: |
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Author:zhbruce |
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Description: 用java实现的神经网络bp的核心算法，主要用于数据预测，总共20个源代码文件，eclipse的该项目，希望对大家有帮助。-Use of neural networks java realize bp core algorithm, mainly used for data prediction, a total of 20 source code files, eclipse of the project, in the hope that everyone has to help. Platform: |
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Author:heyueqiang |
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Description: 以某区水文数据为例，在matlab7中构建BP神经网络，进行仿真预测。-Hydrological data in a certain district, for example, in matlab7 Building BP neural networks, simulation prediction. Platform: |
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Author:猪宝贝 |
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Description: 利用线性神经网络对某一正弦信号进行线性预测。利用函数newlind设计线性神经网络，
在已知正弦信号过去5个值得情况下，预测其将来值。
定义需要的信号，共持续5s，采样频率40Hz-Using linear neural network to a sinusoidal signal for linear prediction. Newlind design using a linear function of neural networks, known sinusoidal signal in the past five worthy cases, forecast its future value. The definition of the needs of the signal, sustained a total of 5s, the sampling frequency of 40Hz Platform: |
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Author:舒 |
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Description: 介绍了数据挖掘的各种方法（如支持向量机，神经网络，遗传算法）在地震预测中的应用-Introduced a variety of data mining methods (such as support vector machines, neural networks, genetic algorithms) in the earthquake prediction Platform: |
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Author:tankai |
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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: |
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Author:11 |
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Description: 在matlab中用lvq神经网络实现交通事故持续时间的预测-In the matlab using lvq neural networks to forecast the duration of the traffic incident Platform: |
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Author:小管 |
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Description: 在Matlab中加入神经网络预测模型，实现对Ad Hoc网络的AODV的预测功能。-In the Matlab neural network prediction model by adding to realize AODV for Ad Hoc networks prediction capabilities. Platform: |
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Author:燕子 |
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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.
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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: |
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Description: 能够实现RBF,GRNN和PNN神经网络的案例，一个是RBF-近红外光谱汽油辛烷值预测，GRNN，PNN-鸢尾花种类识别。代码和数据均有，直接可以拿来使用。(These are cases of RBF, GRNN and PNN neural networks can be realized. One is RBF-NIR spectroscopy gasoline octane prediction, GRNN, PNN-Iris species identification. Both code and data are available and can be used directly.) Platform: |
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Author:悠嘻猴岁月 |
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