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Paper Information

Journal:   INTERNATIONAL JOURNAL OF INDUSTRIAL CHEMISTRY (IJIC)   SEPTEMBER 2011 , Volume 2 , Number 3; Page(s) 177 To 182.
 
Paper: 

A NEW APPROACH TO TRAIN MULTILAYER PERCEPTRON ANN USING ERROR BACK-PROPAGATION AND GENETIC ALGORITHMS HYBRID: A CASE STUDY OF PVTX ESTIMATION OF CH4+CF4 GAS MIXTURE

 
 
Author(s):  MOGHADASSI ABDOLREZA*, NIKKHOLGH MAHMOOD REZA, HOSSEINI SAYED MOHSEN, PARVIZIAN FAHIME, HASHEMI SEYYED JELALADDIN
 
* DEPARTMENT OF CHEMICAL ENGINEERING, FACULTY OF ENGINEERING, ARAK UNIVERSITY, ARAK, IRAN
 
Abstract: 
A new algorithm to train Multilayer Perceptron Artificial Neural Network using the genetic and Error Back-propagation algorithms Hybrid has been devised. The new algorithm solves the local minimum trap as a natural result of the standard numerical optimization based methods and by following the global minimums the ANN training accuracy has been highly improved. There are many algorithms for training a Multilayer Perceptron ANN to estimate the PVTx of CH4+CF4 gas mixture. The new devised algorithm is compared and evaluated against these algorithms and indicates a better accuracy.
 
Keyword(s): ARTIFICIAL NEURAL NETWORK, GAS MIXTURE, GENETIC ALGORITHM, HYBRID, PVTS ESTIMATION
 
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