So far the methods used to predict or calculate the melting point of organic compunds do not focus on the compound nature, they mostly use microscopic physio-chemical properties of materials. In this paper the disadvantage of such traditional methods will be defined. Then a new method is introduced. This method uses the nature properties of compounds to estimate their melting point based on an artificial neural network and offsets the disadvantges of pervious ones.
Published in | Modern Chemistry (Volume 2, Issue 2) |
DOI | 10.11648/j.mc.20140202.12 |
Page(s) | 15-18 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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Copyright © The Author(s), 2014. Published by Science Publishing Group |
Artificial Neural Networks, Neurons, Matlab 2013, Fitnet Function, Levenberg-Marquart Algorithm
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APA Style
Yahya Hassanzadeh-Nazarabadi, S. Majed Modaresi, S. Bahram Jafari, Sanaz Taheri-Boshrooyeh. (2014). Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks. Modern Chemistry, 2(2), 15-18. https://doi.org/10.11648/j.mc.20140202.12
ACS Style
Yahya Hassanzadeh-Nazarabadi; S. Majed Modaresi; S. Bahram Jafari; Sanaz Taheri-Boshrooyeh. Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks. Mod. Chem. 2014, 2(2), 15-18. doi: 10.11648/j.mc.20140202.12
AMA Style
Yahya Hassanzadeh-Nazarabadi, S. Majed Modaresi, S. Bahram Jafari, Sanaz Taheri-Boshrooyeh. Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks. Mod Chem. 2014;2(2):15-18. doi: 10.11648/j.mc.20140202.12
@article{10.11648/j.mc.20140202.12, author = {Yahya Hassanzadeh-Nazarabadi and S. Majed Modaresi and S. Bahram Jafari and Sanaz Taheri-Boshrooyeh}, title = {Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks}, journal = {Modern Chemistry}, volume = {2}, number = {2}, pages = {15-18}, doi = {10.11648/j.mc.20140202.12}, url = {https://doi.org/10.11648/j.mc.20140202.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mc.20140202.12}, abstract = {So far the methods used to predict or calculate the melting point of organic compunds do not focus on the compound nature, they mostly use microscopic physio-chemical properties of materials. In this paper the disadvantage of such traditional methods will be defined. Then a new method is introduced. This method uses the nature properties of compounds to estimate their melting point based on an artificial neural network and offsets the disadvantges of pervious ones.}, year = {2014} }
TY - JOUR T1 - Predicting the Melting Point of Organic Compounds Consist of Carbon, Hydrogen, Nitrogen and Oxygen Using Multi Layer Perceptron Artificial Neural Networks AU - Yahya Hassanzadeh-Nazarabadi AU - S. Majed Modaresi AU - S. Bahram Jafari AU - Sanaz Taheri-Boshrooyeh Y1 - 2014/05/30 PY - 2014 N1 - https://doi.org/10.11648/j.mc.20140202.12 DO - 10.11648/j.mc.20140202.12 T2 - Modern Chemistry JF - Modern Chemistry JO - Modern Chemistry SP - 15 EP - 18 PB - Science Publishing Group SN - 2329-180X UR - https://doi.org/10.11648/j.mc.20140202.12 AB - So far the methods used to predict or calculate the melting point of organic compunds do not focus on the compound nature, they mostly use microscopic physio-chemical properties of materials. In this paper the disadvantage of such traditional methods will be defined. Then a new method is introduced. This method uses the nature properties of compounds to estimate their melting point based on an artificial neural network and offsets the disadvantges of pervious ones. VL - 2 IS - 2 ER -