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Neural Network ñ Comparing the Performances of the Training Functions for Predicting the Value of Specific Heat of Refrigerant in Vapor Absorption Refrigeration System

by Dheerendra Vikram Singh, Govind Maheshwari, Ritu Shrivastav, Durgesh Kumar Mishra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 18 - Number 4
Year of Publication: 2011
Authors: Dheerendra Vikram Singh, Govind Maheshwari, Ritu Shrivastav, Durgesh Kumar Mishra
10.5120/2276-2944

Dheerendra Vikram Singh, Govind Maheshwari, Ritu Shrivastav, Durgesh Kumar Mishra . Neural Network ñ Comparing the Performances of the Training Functions for Predicting the Value of Specific Heat of Refrigerant in Vapor Absorption Refrigeration System. International Journal of Computer Applications. 18, 4 ( March 2011), 1-5. DOI=10.5120/2276-2944

@article{ 10.5120/2276-2944,
author = { Dheerendra Vikram Singh, Govind Maheshwari, Ritu Shrivastav, Durgesh Kumar Mishra },
title = { Neural Network ñ Comparing the Performances of the Training Functions for Predicting the Value of Specific Heat of Refrigerant in Vapor Absorption Refrigeration System },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 18 },
number = { 4 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume18/number4/2276-2944/ },
doi = { 10.5120/2276-2944 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:05:23.873636+05:30
%A Dheerendra Vikram Singh
%A Govind Maheshwari
%A Ritu Shrivastav
%A Durgesh Kumar Mishra
%T Neural Network ñ Comparing the Performances of the Training Functions for Predicting the Value of Specific Heat of Refrigerant in Vapor Absorption Refrigeration System
%J International Journal of Computer Applications
%@ 0975-8887
%V 18
%N 4
%P 1-5
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of this work is to compare performances of three training functions (TRAINBR, TRAINCGB and TRAINCGF) used for training neural network for predicting the value of the specific heat capacity of working fluid, LiBr-H2O, used in vapour absorption refrigeration system. The comparison is shown on the basis of percentage relative error, coefficient of multiple determination R-square, root mean square error and sum of the square due to error.

References
  1. Omer Kaynakli and Recep Yamankaradeniz. 2008. “Thermodynamic analysisof absorption refrigeration system based on entropy generation”. CURRENT SCIENCE. Vol. 92. : 472-479.
  2. P.Bourseau and R.Bugarel. 1986.Absorption-diffusion machines: comparison of performances of NH3-H2O and NH3-NaSCN. Int.J.Refrig. Vol. 206-214.
  3. Soteris Kalogirou. 2007. “Recent Patents in Absorption Cooling Systems”. Bentham Science Publishers Ltd.:58-64.
  4. Misra RD, Sahoo S, Gupta A.2003.Thermoeconomic optimization of a single effect water / LiBr vapour absorption refrigeration system. Int.J.Refrig. Vol.2003:158-69.
  5. Ogulata RT, Doba F. 1998. Experiments and entropy generation minimization of a cross flow heat exchanger. Int.J.Mass Transfer. Vol. 41(2): 373-81.
  6. Vargas J, Bejan A, Siems D. 2001. Integrative thermodynamic optimization ofcross flow heat exchanger for an aircraft environmental control system. J Heat transfer. Vol.128: 760-9. Energs Convers Manage. Vol. 39: 760-9.
  7. Chua HT, Toh HK, Ng KC. 2002. Thermodynamic modeling of an ammonia/water absorption chiller. Int.J.Refrig. Vol. 25: 896-906.
  8. H.T. Chua, H.K. Toh, A. Malek, K.C. Ng , K. Srinivasan (2000):Improved thermodynamic property fields of LiBr-H2O solution”, International Journal of Refrigeration,vol. 23,pp 412-429.
  9. Yasar islamgolu(2003):A New Approach for The Prediction of The Heat Transfer Rate of The Wire-on-Tube Type Heat Exchanger use of An Artificial Neural Network Model, Applied Thermal Engineering,vol 23, pp. 243-249.
  10. Rojalina Priyadarshni, Nillamadhub Dash, Tripti Swarnkar, Rachita Misra(2010): functional analysis of artificial neural network for database classification, IJJCT, vol 1(2,3,4), pp 49-54.
  11. G.N.Xie, Q.W.Wang, M.Zeng, L.Q.Luo (2007): Heat Ttransfer Analysis for Shell and Tube Heat Exchangers with Experimental Data by Artificial Neural Network Approach, Applied Thermal Engineering, vol 27, pp. 1096-1104, 2007.
  12. Obodeh O, Ajuwa, C. I. (2009):Evaluation of Artificial Neural Network Performance in Predicting Diesel Engine NOx Emissions, European Journal of Scientific Research, vol 33(4), pp. 642-653.
  13. Arzu Sencan,Kemal A.yakut, Soteri A. Kalogirou (2006): Thermodynamic analysis of Absorption Systems using Artificial Neural Network, Renewable Energy, vol 31, pp. 29-34.
  14. Adnan Sozen, Mehmet Ozlap, Erol Arcaklioglu (2007): Calculation for the thermodynamic properties for an alternative refrigerant (508a) using artificial neural network, Applied Thermal Engineering, vol 27, pp. 551-559.
  15. C.K.Tan, J.Ward, S.J.Wilox (2009): Artificial Neural Network Modelling Performace of a Compact Heat Exchanger, Applied Thermal Engineering, vol 29, pp. 3609-3617.
  16. Da-Wen Sun (1997): Thermodynamic Design Data and Optimum Design Maps for Absorption Refrigeration Syste “Applied Thermal Engineering,vol 17(3), pp. 211-221.
  17. Soteri A. Kalogirou(2001): Artificial Neural Networks in “Renewable Energy systems and applications:A Review”, Renewable & Sustainable Energy Reviews, vol 5, pp. 373-401.
Index Terms

Computer Science
Information Sciences

Keywords

ANN (Artificial Neural Network) VAR (Vapour Absorption Refrigeration System) R2 (Coefficient of multiple determination) LiBr-H2O

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