http://rdf.ncbi.nlm.nih.gov/pubchem/patent/KR-101325125-B1
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-12 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-12 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F19-00 |
| filingDate | 2011-10-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2013-11-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2013-11-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | KR-101325125-B1 |
| titleOfInvention | Multiple Linear Regression-Artificial Neural Network Hybrid Model Predicting Critical Volume of Pure Organic Compound |
| abstract | The present invention consists of up to five elements such as hydrogen (H), carbon (C), nitrogen (N), oxygen (O), and sulfur (S), and is composed of pure molecules consisting of up to 25 atoms except hydrogen. A mathematical model for predicting the critical volume of organic compounds with high accuracy is provided. The model described above is based on a number of multiplexes, with several of the various molecular descriptors as independent variables and critical volumes as dependent variables, for a number of organic compounds that meet the above conditions for which the experimental value of the critical volume is known. An artificial neural network that obtains the best of the multiple linear regression models by using a genetic algorithm and then outputs the critical volume by inputting the values of the molecular presenters included in the model. It is an example of a quantitative structure-property relationship (QSPR) model, which is a hybrid model of multiple linear regression-artificial neural network that further improves predictive performance by constructing a network. Any molecule predicts the critical volume of a compound made purely of this molecule. As such, the present invention provides a method for predicting the value of the critical volume that is reliable even for a large number of organic compounds in the above-mentioned conditions, in which the experimental value is unknown, thereby saving the cost and time required for the experiment, and thus promoting R & D activities in related industries. It produces effects such as facilitating. |
| priorityDate | 2011-10-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 22.