搜索资源列表
Model_ARIMA1
- 季节性移动自回归模型 可以进行时间序列的预测 尤其是季节性数据-S-Arima seaonal Arima model in matlab
greymodel
- GM(1,1),用于数据的预测，是灰色系通过理论的一个模型-greymodel
21
- 基于数据挖掘的国际贸易客户流失的预测分析 的论文内含程序-Based on the International Trade Data Mining forecast churn papers containing analysis of the procedures
height
- 根据父母的身高数据以及本人的性别实现身高的预测-According to the parents of the height data as well as my prediction of gender to achieve height
BP
- 用于灰色预测，可以精确预测，尤其适于大量数据的预测-For gray prediction can accurately predict
BP-network
- bp 神经网络源代码，可直接用于工程预测，比如沉降观测数据的预测-net work
neural-network-model
- 这是我用遗传算法对神经网络模型进行的优化，可以实现对某些数据的预测-This is my genetic algorithm with optimization neural network model can be achieved on some of the data to predict
Gray
- 用于数据的预测，通常通过已知的几个数据预测后边的数据，适合少量数据-Prediction for data, typically by several known predictive data of the data back, suitable for small amounts of data
LSSVR
- 此段代码是最小二乘支持向量机的源代码，功能是实现某些样本数据的预测-This code is the least squares support vector machine (SVM) of the source code, function is to realize the prediction of some sample data
大数据时代
- 大数据的核心就是预测。它通常被视为人工智能的一部分，或者更确切地说，被视为一种 机器学习。但是这种定义是有误导性的。大数据不是要教机器像人一样思考。相反，它是把数学算法运用到海量的数据上来预测事情发生的可能性。(The heart of big data is prediction. It is usually regarded as part of artificial intelligence, or rather, considered as a kind of artificial in
huise test
- 根据已知的数据来预测下一阶段的数据，可以预测多个数据。(Based on known data to predict the next phase of the data, you can predict a number of data.)
fx预测去噪方法
- 利用fx域的预测反褶积可以有效的去除随机噪声，内附数据，能运行调试(Predictive deconvolution using FX domain can effectively remove random noise, enclosing data, can run debugging)
Greynet
- 数学模型，将GM灰度模型与BP神经网络进行融合，可用于对数据的预测(The mathematical model combines the GM gray model with the BP neural network, and can be used to predict the data)
SVM
- SVM预测小程序，方便理解,所以很好 值得去观看 用的很好(this is a SVM process)
BP神经网络负荷预测代码
- 能够对一天之中每隔15min的电力负荷数据进行预测(load forecast based on BP neural network)
bp神经网络
- 运用bp神经网络建立预测模型，通过matlab实现未知数据的预测(The bp neural network is used to establish the prediction model and the unknown data is predicted by matlab.)
BP神经网络预测激光焊接数据
- 建立BP神经网络拟合曲面，实现激光焊接数据的预测(BP neural network mainly through 4 steps in the implementation of MATLAB, read file setting parameters, create a BP network, neural network training, neural network simulation. After the program, some basic parameters are set
采用BP神经网络进行非线性预测
- 该代码包括单隐含层BP和双隐含层BP。建立基于BP神经网络的预测模型，对数据进行随机排列，选取训练样本和测试样本，训练样本训练网络，测试样本进行验证(The code includes single hidden layer BP and double hidden layer BP. Establish a prediction model based on BP neural network, arrange the data randomly, select training sample
基于极限学习机的预测
- 针对非线性预测问题，建立极限学习机的预测模型，将数据样本分为训练样本和测试样本，并采用误差指标进行评价。(Aiming at the problem of non-linear prediction, the prediction model of extreme learning machine is established. The data samples are divided into training samples and test samples, and the error i
BP负荷预测
- 利用神经网络算法进行负荷的预测，选取若干天的预测数据，来预测接下来几天的负荷数据(The neural network algorithm is used to forecast the load. The forecasting data of several days are selected to forecast the load data of the next few days)