MATLAB 预测模型免费分享

上传者: java_cjkl | 上传时间: 2023-07-06 10:45:13 | 文件大小: 329KB | 文件类型: RAR
灰色预测是一种对含有不确定因素的系统进行预测的方法。灰色预测通过鉴别系统因素之间发展趋势的相异程度,即进行关联分析,并对原始数据进行生成处理来寻找系统变动的规律,生成有较强规律性的数据序列,然后建立相应的微分方程模型,从而预测事物未来发展趋势的状况。其用等时距观测到的反映预测对象特征的一系列数量值构造灰色预测模型,预测未来某一时刻的特征量,或达到某一特征量的时间。基本思想: ARIMA模型的全称叫做自回归移动平均模型,全称是(ARIMA, Autoregressive Integrated Moving Average Model)。也记作ARIMA(p,d,q),是统计模型(statistic model)中最常见的一种用来进行时间序列 预测的模型。 基本步骤: 1)导入实验数据。2)确定ARMA模型阶数。3)残差检验。4)给出结果 微分方程模型是我们在日常生活中比较常见并且比较重要的一种模型,我们在平时的课程中时经常会涉及到这种题型,像比如我们所遇到的牛顿第二定律就常遇到相关的问题。适用于基于相关原理的因果预测模型,大多是物理或几何方面的典型问题,假设条件,用数学符号表示规律,

文件下载

资源详情

[{"title":"( 45 个子文件 329KB ) MATLAB 预测模型免费分享","children":[{"title":"MATLAB 预测模型","children":[{"title":"7.MATLAB预测与预报模型代码 基于单层竞争神经网络的患者癌症发病预测代码","children":[{"title":"gene.mat <span style='color:#111;'> 23.33KB </span>","children":null,"spread":false},{"title":"gene.txt <span style='color:#111;'> 46.57KB </span>","children":null,"spread":false},{"title":"chapter16.m <span style='color:#111;'> 2.90KB </span>","children":null,"spread":false}],"spread":true},{"title":"9.MATLAB预测与预报模型代码 基于灰色神经网络的订单需求预测代码","children":[{"title":"Greynet.m <span style='color:#111;'> 3.08KB </span>","children":null,"spread":false},{"title":"data.mat <span style='color:#111;'> 1.47KB </span>","children":null,"spread":false}],"spread":true},{"title":"3.MATLAB预测与预报模型代码 基于AR预测模型的未来油价预测代码","children":[{"title":"08582053AR","children":[{"title":"AR.m <span style='color:#111;'> 15.69KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"1.MATLAB预测与预报模型代码 微粒群算法结合灰色系统理论进行预测","children":[{"title":"plotljz.m <span style='color:#111;'> 176B </span>","children":null,"spread":false},{"title":"hundun.m <span style='color:#111;'> 331B </span>","children":null,"spread":false},{"title":"www.downma.com.txt <span style='color:#111;'> 151B </span>","children":null,"spread":false},{"title":"main.m <span style='color:#111;'> 1.88KB </span>","children":null,"spread":false},{"title":"minf.m <span style='color:#111;'> 150B </span>","children":null,"spread":false},{"title":"huise.m <span style='color:#111;'> 1.31KB </span>","children":null,"spread":false}],"spread":true},{"title":"5.MATLAB预测与预报模型代码 基于Logistic回归模型评估企业还款能力代码","children":[{"title":"数据.xlsx <span style='color:#111;'> 9.24KB </span>","children":null,"spread":false},{"title":"Logistic回归模型代码.txt <span style='color:#111;'> 639B </span>","children":null,"spread":false}],"spread":true},{"title":"10.MATLAB预测与预报模型代码 离散灰色预测模型和AR预测模型的组合预测","children":[{"title":"www.downma.com.txt <span style='color:#111;'> 153B </span>","children":null,"spread":false},{"title":"灰色.m <span style='color:#111;'> 1.17KB </span>","children":null,"spread":false}],"spread":true},{"title":"2.MATLAB预测与预报模型代码 混沌时间序列的RBF神经网络预测代码","children":[{"title":"www.downma.com.txt <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"Prediction_RBF","children":[{"title":"normalize_a.p <span style='color:#111;'> 1.27KB </span>","children":null,"spread":false},{"title":"normalize_1.p <span style='color:#111;'> 1.15KB </span>","children":null,"spread":false},{"title":"Main_RBF.m <span style='color:#111;'> 1.89KB </span>","children":null,"spread":false},{"title":"Main_RBF_MultiStepPred.m <span style='color:#111;'> 2.11KB </span>","children":null,"spread":false},{"title":"LorenzData.mexw32 <span style='color:#111;'> 20.00KB </span>","children":null,"spread":false},{"title":"PhaSpaRecon.p <span style='color:#111;'> 3.22KB </span>","children":null,"spread":false},{"title":"Contents.m <span style='color:#111;'> 49B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"8.MATLAB预测与预报模型代码 基于广义回归神经网络货运量预测代码","children":[{"title":"chapter8.2.m <span style='color:#111;'> 2.50KB </span>","children":null,"spread":false},{"title":"chapter8.1.m <span style='color:#111;'> 4.04KB </span>","children":null,"spread":false},{"title":"best.mat <span style='color:#111;'> 1.20KB </span>","children":null,"spread":false},{"title":"运行提示.txt <span style='color:#111;'> 198B </span>","children":null,"spread":false},{"title":"chapter8.2.asv <span style='color:#111;'> 2.50KB </span>","children":null,"spread":false},{"title":"data.mat <span style='color:#111;'> 815B </span>","children":null,"spread":false}],"spread":true},{"title":"6.MATLAB预测与预报模型代码 基于SVM神经网络的上证开盘指数预测回归预测分析代码","children":[{"title":"chapter14_sh.mat <span style='color:#111;'> 214.82KB </span>","children":null,"spread":false},{"title":"chapter14.m <span style='color:#111;'> 7.06KB </span>","children":null,"spread":false},{"title":"html","children":[{"title":"chapter14_03.png <span style='color:#111;'> 17.70KB </span>","children":null,"spread":false},{"title":"chapter14_06.png <span style='color:#111;'> 10.23KB </span>","children":null,"spread":false},{"title":"chapter14_04.png <span style='color:#111;'> 25.04KB </span>","children":null,"spread":false},{"title":"chapter14.html <span style='color:#111;'> 25.59KB </span>","children":null,"spread":false},{"title":"chapter14.png <span style='color:#111;'> 3.85KB </span>","children":null,"spread":false},{"title":"chapter14_05.png <span style='color:#111;'> 11.61KB </span>","children":null,"spread":false},{"title":"chapter14_02.png <span style='color:#111;'> 8.63KB </span>","children":null,"spread":false},{"title":"chapter14_01.png <span style='color:#111;'> 8.28KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"4.MATLAB预测与预报模型代码 基于BP_Adaboost算法的公司财务预警建模代码","children":[{"title":"Bp_Ada_Fore.m <span style='color:#111;'> 1.79KB </span>","children":null,"spread":false},{"title":"Bp_Ada_Sort.m <span style='color:#111;'> 2.24KB </span>","children":null,"spread":false},{"title":"data1.mat <span style='color:#111;'> 45.31KB </span>","children":null,"spread":false},{"title":"data.mat <span style='color:#111;'> 11.54KB </span>","children":null,"spread":false}],"spread":true},{"title":"12.MATLAB预测与预报模型代码 马尔科夫链法预测股票.rar <span style='color:#111;'> 7.71KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明