• 主题
高级搜索  >
历史搜索 清空历史
首页> 外国专利> MULTIPLE LINEAR REGRESSION-ARTIFICIAL NEURAL NETWORK HYBRID MODEL FOR PREDICTING THE CRITICAL VOLUME OF PURE ORGANIC COMPOUNDS CAPABLE OF FORMING AN ARTIFICIAL NEURAL NETWORK OUTPUTTING THE CRITICAL VOLUME BASED ON THE VALUES OF MOLECULAR DESCRIPTORS CONTAINED IN A MULTIPLE LINEAR REGRESSION MODEL

MULTIPLE LINEAR REGRESSION-ARTIFICIAL NEURAL NETWORK HYBRID MODEL FOR PREDICTING THE CRITICAL VOLUME OF PURE ORGANIC COMPOUNDS CAPABLE OF FORMING AN ARTIFICIAL NEURAL NETWORK OUTPUTTING THE CRITICAL VOLUME BASED ON THE VALUES OF MOLECULAR DESCRIPTORS CONTAINED IN A MULTIPLE LINEAR REGRESSION MODEL

机译：多个线性回归-人工神经网络混合模型，用于预测能够形成人工神经网络的纯有机化合物的临界体积，该纯神经化合物可以输出基于整数的基于整数的值的基于分子的临界体积

页面导航
  • 摘要
  • 著录项
  • 相似文献

摘要

PURPOSE: A multiple linear regression-artificial neural network(MLR-ANN) hybrid model for predicting the critical volume of pure organic compounds is provided to improve the performance of prediction.;CONSTITUTION: Experimental data for hydrocarbon-based compounds is input. Molecular descriptors for the critical volume of the hydrocarbon-based compounds are prepared. The experimental data is classified based on a training set and a testing set. The optimal MLR model(MLRM) for the training set is searched. Entire samples are divided into three sets, and the optimal ANN model(ANNM) is searched. If the absolute value of the predicted critical volume difference based on the optimal MLRM and the optimal ANNM is more than an over-fitting preventive reference value, the predicted critical volume based on the MLRM is adopted as the critical volume.;COPYRIGHT KIPO 2012
机译：目的：提供一种用于预测纯有机物临界体积的多元线性回归人工神经网络（MLR-ANN）混合模型，以提高预测性能。;组成：输入烃基化合物的实验数据。制备了临界体积的基于烃的化合物的分子描述符。根据训练集和测试集对实验数据进行分类。搜索训练集的最佳MLR模型（MLRM）。将整个样本分为三组，并搜索最优的神经网络模型（ANNM）。如果基于最佳MLRM和最佳ANNM的预测临界体积差异的绝对值大于过拟合预防参考值，则将基于MLRM的预测临界体积用作临界体积。; COPYRIGHT KIPO 2012

著录项

相似文献

  • 专利
  • 外文文献
  • 中文文献
获取专利

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号