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Description: 利用灰色系统进行预测的几篇好论文:
BP神经网络_灰色系统联合模型预测软基沉降量 非线性时间序列神经网络预测方法的研究及应用 股票投资价值灰色马尔可夫预测 股票投资价值灰色系统模型及应用 灰色关联神经网络模型在股指预测中的应用 灰色理论与模型及在车辆拥有量预测中的应用 灰色神经网络交通事故预测比较 灰色神经网络预测模型的应用 灰色-神经网络综合预测模型-Gray prediction system using a few good papers: BP neural network system _ a joint model gray soft ground settlement prediction of nonlinear time series prediction method of neural network research and application of the gray value of equity investments Markov prediction value of the equity investments of the gray system Application of gray relational model and neural network model in forecasting stock gray theory and model and prediction of vehicle ownership in the application of gray neural network traffic prediction compare gray neural network prediction model of the application of gray- the integrated neural network prediction model
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Size: 883712 |
Author: yujian |
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Description: 这是《精通VC++数字图像模式识别技术及工程实践[第2版]》光盘源代码，其中包括EM算法、fisher判别函数、HMM隐马尔科夫模型、BP神经网络、小波变换、alpha-beta剪枝、A*算法等，还包含几个纹理分析、人脸定位、字符识别、车牌号识别、8数码、黑白棋、离线/在线签名等实例，因此对于学习模式识别、人工智能的朋友们都大有裨益。光盘中的素材请见另外一个资源。-This is " proficient in VC++ Digital Image Pattern Recognition Technology and Engineering Practice [2nd edition]" CD-ROM source code, including the EM algorithm, fisher discriminant function, HMM hidden Markov model, BP neural network, wavelet transform, alpha- beta pruning, A* algorithm, but also contains a number of texture analysis, face positioning, character recognition, license plate number recognition, digital 8, Reversi, offline/online signatures examples, so for studying pattern recognition, artificial intelligence friends are of great benefit. CD-ROM material, see the other resources.
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Size: 14680064 |
Author: 吴 |
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Description: Markov随机场的例子程序，包括ICM，BP算法，matlab编写，共30几个函数。-Markov random field examples of procedures, including ICM, BP algorithm, matlab prepared a total of 30 number of functions.
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Size: 18432 |
Author: tlh |
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Description: Belief Propagation (BP) Implementations
gabp.m, run_gabp.m => Gaussian BP - parallel version
asynch_GBP.m => Gaussian BP - serial version
sparse_gabp.m, run_sparse_gabp.m => Gaussian BP - sparse version, optimized, tested on sparse matrices
gabpms.ms, run_gabpms.m => Quadratic Min-Sum algorithm - Moallemi and Van-Roy
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Size: 224256 |
Author: savsee |
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Description: 结合马尔科夫和BP神经网络模型的预测决策支持系统-Combined Markov and BP neural network model predictive decision support system
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Size: 1682432 |
Author: 陈晨 |
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Description: 针对中长期电力负荷预测样本量小、多因素影响的特点,利用灰色关联度筛选影响因素,建立基于BP
神经网络算法的负荷预测模型,通过多因素变量及历史负荷变量序列进行滚动预测,得到的预测值明显优于
单一预测方法,并通过马尔可夫过程对预测残差进行修正,使预测精度得到较大提高,研究实证表明,这种预
测方法具有进行推广应用的价值
-For long-term load forecasting small sample size, the characteristics of many factors, the use of gray correlation screening factors is established based on BP
Neural network algorithm for load forecasting model, the multivariable and historical load variable sequences rolling forecasts, significantly better than the predicted values obtained
Single forecasting methods, and through the Markov process to amend the prediction residuals, the prediction accuracy is greatly improved, empirical studies show that pre-
Method for measuring the value of popularization and application
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Size: 2048 |
Author: lhj |
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Description: 将置信传播(belief propagation,BP)算法从马尔科夫随机域的角度进行理解，
并通过变量节点和校验界定之间的迭代来实现信息传递，进而提高系统的误码率性能。
-The belief propagation (belief propagation, BP) algorithm is understood from the perspective of Markov random field, and by defining the iteration variable nodes and check to realize the transmission of information between, and to improve the BER performance of the system.
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Size: 1024 |
Author: 王文 |
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Description: This code perform stereo matching process under Markov Random Field and Loopy Belief Propagation.
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Size: 353280 |
Author: peymanr |
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Description: 这是一种基于近红外光谱的非线性建模方法及系统，从各所述近红外光谱数据随机挑出一部分作为校正集，挑出一部分作为验证集；将所述校正集和所述验证集通过主成分分析得到光谱特征空间；在所述光谱特征空间中，通过马氏距离法选取所述校正集里与所述验证集的各个样本最近似的样本作为校正子集；从所述校正子集中提取主成分数，作为BP神经网络的输入层建立回归模型，不仅能解决各因素之间多重相关的问题，还避免了大量的噪声和一些无用的信息，降低了变量维数，在BP神经网络的非线性映射能力和适应学习能力的基础上，提高了模型的预测稳定性和精度。-This is a kind of nonlinear modeling method and system based on near infrared spectrum, described the near infrared spectrum data randomly selected part as calibrating, pick out the part as a validation set Will be described in the calibration set and described in the validation set is obtained by principal component analysis (spectral feature space It is spectral feature space, the selection method described by markov distance calibration set and validation set described in each sample with the sample as the calibration subsets Principal components extracted calibration described subset, as BP neural network input layer to establish a regression model, not only can solve the problem of multiple correlation among various factors, also avoid a lot of noise and some useless information, reducing the variable dimension, the nonlinear mapping ability of BP neural network and adaptive learning ability, on the basis of improve stability and accuracy of the prediction of the model.
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Size: 2048 |
Author: 詹映 |
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