The Impact of the Number of Neurons in the Hidden Layer on the Performance of MLP Neural Network: Application to the Fast Identification of Toxic Gases
In this work, neural networks methods MLP type were
applied to a database from an array of six sensors for the detection of
three toxic gases. The choice of the number of hidden layers and the
weight values are influential on the convergence of the learning
algorithm. We proposed, in this article, a mathematical formula to
determine the optimal number of hidden layers and good weight
values based on the method of back propagation of errors. The results
of this modeling have improved discrimination of these gases and
optimized the computation time. The model presented here has
proven to be an effective application for the fast identification of
toxic gases.
[1] M. W. Gardner, S.R. Dorling, “Neural network modeling and prediction
of hourly NOX and NO2 concentrations in urban air in London,”
Atmospheric Environment, 33, 5, pp. 709–719, 1999.
[2] Kukkonen,et al, “Extensive evaluation of neural network models for the
prediction of NO2 and PM10 concentrations compared with a
deterministic modeling system and measurements in central Helsinski,”
Atmospheric Environment,37,pp. 4539–4550, 2003.
[3] D. Jiang, Y. Zhang, X. Hu, Y. Zeng, J. Tan, D. Shao, “in developing an
ANN model for air pollution index forecast,” Atmospheric Environment,
38,pp. 7055–7064, 2004.
[4] U. Brunelli, V. Piazza, L. Pignato, F. Sorbello, S. Vitabile, “Two-day
ahead prediction of daily maximum concentrations of SO2, O3, PM10,
NO2, CO in the urban area of Palermo,” Atmospheric Environment,
41,pp. 2967–2995, 2007.
[5] K.D. Karatzas, S. Kaltsatos, “Air pollution modelling with the aid of
computational intelligence methods,” in Thessaloniki. Greece,
Simulation Modelling Practice and Theory, pp. 1310–1319, doi.
10.1016/ j.simpat.2007.09.005.
[6] J. Galloway, et al, “Nitrogen cycles: past, present and future,”
Biogeochemistry, 70, pp.153–226, 2004.
[7] H.J.S. Fernando, et al, “Forecasting PM10 in metropolitan areas:
Efficacy of neural networks,” Environmental Pollution, 163, pp.62-67,
2012.
[8] J. Fenger, “Air pollution in the last 50 years—from local to global,”
Atmos Environ, pp. 13–22, 2009.
[9] D. Gacquer, et al, “Comparative study of supervised classification
algorithms for the detection of atmospheric pollution,” Engineering
Applications of Artificial Intelligence, 24, pp. 1070–1083, 2011.
[10] A.B. Chelani, M.Z. Hasan, “Forecasting nitrogen dioxide concentration
in ambient air using artificial neural-networks,” International Journal of
Environmental Studies, 58, 4,pp. 487–499, 2001.
[11] K.M. Venables. et al, “Thunderstorm-related asthma - the epidemic of
24/25 June 1994,” Clinical and Experimental Allergy, 27, pp.725-736,
1997.
[12] N. Lokeshwari, G. Srinikethan, V. S. Hegde, “Management of Air
Pollutants from Point Sources,” International Journal of Environmental
Earth Science and Engineering, Vol.7, pp.12, 2013.
[13] J. Burlakovs, M. Klavins, R. Ernsteins, A. Ruskulis, “Contamination in
Industrial Areas and Environmental Management in Latvia,” World
Academy of Science. Engineering and Technology, Vol.76, pp.04-23,
2013.
[14] R. Lalauze, C. Pijolat, “A new approach to selective detection of gas by
an SnO2 solid-state sensor,” Sensors and Actuators, 5, pp. 55–63, 1984.
[15] P. Romppainen, V. Lantto, S. Leppavuori, “Effect of water vapour on the
CO response behavior of tin dioxide sensors in constant temperature and
temperature-pulsed modes of operation,” Sensors and Actuators, B.
Chemicall, pp. 73–78, 1990.
[16] P. van Geloven, M. Honore, J. Roggen, “The influence of relative
humidity on the response of tin oxide gas sensors to carbon monoxide,”
Sensors and Actuators, B. Chemical, 4,pp.185–188, 1991.
[17] J. Watson, K. Ihokura, G.S.V. Coles, “ The tin dioxide gas sensor,”
Measurement Science and Technology, 4,pp. 711–719, 1993.
[18] H. Yamaura, T. Jinkawa, J. Tamaki, K. Moriya, N. Miura, N.
Yamazoe,“Indium oxide-based gas sensor for selective detection of CO,”
Sensors and Actuators, B: Chemical, 35–56,pp.325–332, 1996.
[19] H. Debeda, P. Massok, C. Lucati, F. Menil, J.L. Aucouturier, “Methane
sensing:from sensitive thick films to a reliable selective device,”
Measurement Science and Technology,vol.8, pp.99–110, 1997.
[20] J. Judith Vijaya, L. John Kennedy, G. Sekaran, K.S. Nagaraja,
“Utilization of Sr(II)-added calcium aluminate for the detection of
volatile organic compounds,” Industrial and Engineering Chemistry
Research, vol. 46.19,pp. 6251–6258, 2007.
[21] B.W. Licznerski, K. Nitsch, H. Teterycz, T. Soba´nski, K. Wi´sniewski,
“Characterization of electrical parameters for multilayer SnO2gas
sensors,” Sensors and Actuators B: Chemical. vol. 103, 1–2, pp. 69–75,
2004.
[22] A. Gulbag, F. Temurtas, “A study on quantitative classification of binary
gas mixture using neural networks and adaptive neuro -fuzzy inference
systems,” Sensors and Actuators.B. Chemical, vol. 115, pp. 252–262,
2006.
[23] U. Brunelli, V. Piazza, L. Pignato, F. Sorbello, S. Vitabile, “days ahead
prediction of daily maximum concentrations of SO2, O3, PM10, NO2,
CO in th eurban area of Palermo.Italy,”. Atmospheric Environment,
vol.41,pp.2967–2995, 2007.
[24] M. Kolehmainen, H. Martikainen, J. Ruuskanen, “Neural networks and
periodic components used in air quality forecasting,” Atmospheric
Environment, vol. 35, pp. 815–825, 2001.
[25] J. Yi, V.R. Prybutok, “A neural network model forecasting for prediction
of daily maximum ozone concentration in an industrialized urban area,”
Environmental Pollution, vol. 92, pp. 349–357, 1996.
[26] E. I. Vlahogianni, “Optimized and meta-optimized neural networks for
short-term traffic flow prediction: A genetic approach,” Transportation
Research Part C, vol. 1, pp. 211–234, 2005.
[27] A. Eswaradass, X.-H. Sun, M. Wu, “A neural network based predictive
mechanism for available bandwidth”, proceeding of 19th, IEEE
International Conference on Parallel and Distributed Proceeding
Symposium,Workshop.10,vol.11,p.228,2005..
doi>10.1109/IPDPS.p.234.2005.
[28] H. Yousefia, “Utilizing neural networks to reduce packet loss in selfsimilar
tele traffic,” proceeding of IEEE International. Conference on
Communications, vol.3.pp. 1942–1946, 2003.
[29] M. Badura, et al, “Statistical assessment of quantification methods used
in gas sensor system”, Sensors and Actuators: B. Chemical, vol.188,
pp.815– 823, November 2011.
[30] P. Chandra and Y. Singh, "An activation function adapting training
algorithm for sigmoidal feedforward networks," Neurocomputing,
vol.61, pp. 429–437, 2004.
[31] W. Duch and N. Jankowski, “Survey of neural transfer functions”,
Neural Comput Appl, vol. 2, pp.163–212, 1999.
[32] W. Duch and N. Jankowski, “Transfer functions: Hidden possibilities for
better neural networks,” In 9th European symposium on artificial neural
networks, pp. 81–94, 2001.
[33] Y. Singh and P. Chandra, “A class +1 sigmoidal activation functions for
FFANNs,” J Econ Dynamic Control, vol. 28, pp.183–187, 2003.
[34] B.W. Licznerski, C K. Nitsch, H. Teterycz, T. Sobanski, K. Wisniewski,
“haracterisation of electrical parameters for multilayer SnO2gas
sensors”, Sensors and Actuators. B. Chemical,vol.103, No. 1–2, pp. 69–
75, 2004.
[35] A. Lfakir, “Identification and Quantification of a complex gaseous
atmosphere with an intelligent multi-sensor system. Application to
Detection of mixtures compounds H2S, NO2, SO2 wet atmosphere
variable”, Thesis of Metz University, 2006. “Identification et
Quantification d’une atmosphère gazeuse complexe à l’aide d’un
système multi-capteurs intelligent. Application La détection de mélanges
composés de H2S, NO2, SO2 en atmosphère humide variable”, thèse
d’Université Metz, 2006.
[36] O. Helli, “Gas multi -sensor for the design of an electronic nose for air
pollution monitoring”, Thesis of Metz University, 2003. “Multi-capteurs
de gaz pour la conception d'un nez électronique de surveillance de la
pollution atmosphérique”, thèse d’Université Metz, 2003.
[37] T. Seiyama, N. Yamazoe, S. Yamauchi,Chemical Sensor Technology,
Vol. 1-4, Elsevier Science Ltd, 1988.
[38] Rumelhart, D.E. Hinton, G.E. Williams, “Learning internal
representations by error propagation,” In Rumelhart, D.E. Mc Cleland,
J.L. (Eds.), Parallel Distributed Processing. MIT Press, Cambridge, pp.
318–362, 1986.
[39] W.S. McCulloch, W.H. Pitts, “A logical calculus of the ideas immanent
in nervous activity”, Bulletin of Mathematical Biophysics, vol 5, pp.
115–133, 1943.
[40] Cybenko, “Approximation by superposition of a sigmoidal function, ”
Math. Cont. Sig. Syst 2, pp. 303–314.
[41] S. Ouhmad, H. Roukhe, A, Roukhe “Real time identification of toxic
gases based on artificial neural networks, ” International Journal of
Computational Engineering Research,vol. 04, issue. 4, pp. 67-72, 2014.
[1] M. W. Gardner, S.R. Dorling, “Neural network modeling and prediction
of hourly NOX and NO2 concentrations in urban air in London,”
Atmospheric Environment, 33, 5, pp. 709–719, 1999.
[2] Kukkonen,et al, “Extensive evaluation of neural network models for the
prediction of NO2 and PM10 concentrations compared with a
deterministic modeling system and measurements in central Helsinski,”
Atmospheric Environment,37,pp. 4539–4550, 2003.
[3] D. Jiang, Y. Zhang, X. Hu, Y. Zeng, J. Tan, D. Shao, “in developing an
ANN model for air pollution index forecast,” Atmospheric Environment,
38,pp. 7055–7064, 2004.
[4] U. Brunelli, V. Piazza, L. Pignato, F. Sorbello, S. Vitabile, “Two-day
ahead prediction of daily maximum concentrations of SO2, O3, PM10,
NO2, CO in the urban area of Palermo,” Atmospheric Environment,
41,pp. 2967–2995, 2007.
[5] K.D. Karatzas, S. Kaltsatos, “Air pollution modelling with the aid of
computational intelligence methods,” in Thessaloniki. Greece,
Simulation Modelling Practice and Theory, pp. 1310–1319, doi.
10.1016/ j.simpat.2007.09.005.
[6] J. Galloway, et al, “Nitrogen cycles: past, present and future,”
Biogeochemistry, 70, pp.153–226, 2004.
[7] H.J.S. Fernando, et al, “Forecasting PM10 in metropolitan areas:
Efficacy of neural networks,” Environmental Pollution, 163, pp.62-67,
2012.
[8] J. Fenger, “Air pollution in the last 50 years—from local to global,”
Atmos Environ, pp. 13–22, 2009.
[9] D. Gacquer, et al, “Comparative study of supervised classification
algorithms for the detection of atmospheric pollution,” Engineering
Applications of Artificial Intelligence, 24, pp. 1070–1083, 2011.
[10] A.B. Chelani, M.Z. Hasan, “Forecasting nitrogen dioxide concentration
in ambient air using artificial neural-networks,” International Journal of
Environmental Studies, 58, 4,pp. 487–499, 2001.
[11] K.M. Venables. et al, “Thunderstorm-related asthma - the epidemic of
24/25 June 1994,” Clinical and Experimental Allergy, 27, pp.725-736,
1997.
[12] N. Lokeshwari, G. Srinikethan, V. S. Hegde, “Management of Air
Pollutants from Point Sources,” International Journal of Environmental
Earth Science and Engineering, Vol.7, pp.12, 2013.
[13] J. Burlakovs, M. Klavins, R. Ernsteins, A. Ruskulis, “Contamination in
Industrial Areas and Environmental Management in Latvia,” World
Academy of Science. Engineering and Technology, Vol.76, pp.04-23,
2013.
[14] R. Lalauze, C. Pijolat, “A new approach to selective detection of gas by
an SnO2 solid-state sensor,” Sensors and Actuators, 5, pp. 55–63, 1984.
[15] P. Romppainen, V. Lantto, S. Leppavuori, “Effect of water vapour on the
CO response behavior of tin dioxide sensors in constant temperature and
temperature-pulsed modes of operation,” Sensors and Actuators, B.
Chemicall, pp. 73–78, 1990.
[16] P. van Geloven, M. Honore, J. Roggen, “The influence of relative
humidity on the response of tin oxide gas sensors to carbon monoxide,”
Sensors and Actuators, B. Chemical, 4,pp.185–188, 1991.
[17] J. Watson, K. Ihokura, G.S.V. Coles, “ The tin dioxide gas sensor,”
Measurement Science and Technology, 4,pp. 711–719, 1993.
[18] H. Yamaura, T. Jinkawa, J. Tamaki, K. Moriya, N. Miura, N.
Yamazoe,“Indium oxide-based gas sensor for selective detection of CO,”
Sensors and Actuators, B: Chemical, 35–56,pp.325–332, 1996.
[19] H. Debeda, P. Massok, C. Lucati, F. Menil, J.L. Aucouturier, “Methane
sensing:from sensitive thick films to a reliable selective device,”
Measurement Science and Technology,vol.8, pp.99–110, 1997.
[20] J. Judith Vijaya, L. John Kennedy, G. Sekaran, K.S. Nagaraja,
“Utilization of Sr(II)-added calcium aluminate for the detection of
volatile organic compounds,” Industrial and Engineering Chemistry
Research, vol. 46.19,pp. 6251–6258, 2007.
[21] B.W. Licznerski, K. Nitsch, H. Teterycz, T. Soba´nski, K. Wi´sniewski,
“Characterization of electrical parameters for multilayer SnO2gas
sensors,” Sensors and Actuators B: Chemical. vol. 103, 1–2, pp. 69–75,
2004.
[22] A. Gulbag, F. Temurtas, “A study on quantitative classification of binary
gas mixture using neural networks and adaptive neuro -fuzzy inference
systems,” Sensors and Actuators.B. Chemical, vol. 115, pp. 252–262,
2006.
[23] U. Brunelli, V. Piazza, L. Pignato, F. Sorbello, S. Vitabile, “days ahead
prediction of daily maximum concentrations of SO2, O3, PM10, NO2,
CO in th eurban area of Palermo.Italy,”. Atmospheric Environment,
vol.41,pp.2967–2995, 2007.
[24] M. Kolehmainen, H. Martikainen, J. Ruuskanen, “Neural networks and
periodic components used in air quality forecasting,” Atmospheric
Environment, vol. 35, pp. 815–825, 2001.
[25] J. Yi, V.R. Prybutok, “A neural network model forecasting for prediction
of daily maximum ozone concentration in an industrialized urban area,”
Environmental Pollution, vol. 92, pp. 349–357, 1996.
[26] E. I. Vlahogianni, “Optimized and meta-optimized neural networks for
short-term traffic flow prediction: A genetic approach,” Transportation
Research Part C, vol. 1, pp. 211–234, 2005.
[27] A. Eswaradass, X.-H. Sun, M. Wu, “A neural network based predictive
mechanism for available bandwidth”, proceeding of 19th, IEEE
International Conference on Parallel and Distributed Proceeding
Symposium,Workshop.10,vol.11,p.228,2005..
doi>10.1109/IPDPS.p.234.2005.
[28] H. Yousefia, “Utilizing neural networks to reduce packet loss in selfsimilar
tele traffic,” proceeding of IEEE International. Conference on
Communications, vol.3.pp. 1942–1946, 2003.
[29] M. Badura, et al, “Statistical assessment of quantification methods used
in gas sensor system”, Sensors and Actuators: B. Chemical, vol.188,
pp.815– 823, November 2011.
[30] P. Chandra and Y. Singh, "An activation function adapting training
algorithm for sigmoidal feedforward networks," Neurocomputing,
vol.61, pp. 429–437, 2004.
[31] W. Duch and N. Jankowski, “Survey of neural transfer functions”,
Neural Comput Appl, vol. 2, pp.163–212, 1999.
[32] W. Duch and N. Jankowski, “Transfer functions: Hidden possibilities for
better neural networks,” In 9th European symposium on artificial neural
networks, pp. 81–94, 2001.
[33] Y. Singh and P. Chandra, “A class +1 sigmoidal activation functions for
FFANNs,” J Econ Dynamic Control, vol. 28, pp.183–187, 2003.
[34] B.W. Licznerski, C K. Nitsch, H. Teterycz, T. Sobanski, K. Wisniewski,
“haracterisation of electrical parameters for multilayer SnO2gas
sensors”, Sensors and Actuators. B. Chemical,vol.103, No. 1–2, pp. 69–
75, 2004.
[35] A. Lfakir, “Identification and Quantification of a complex gaseous
atmosphere with an intelligent multi-sensor system. Application to
Detection of mixtures compounds H2S, NO2, SO2 wet atmosphere
variable”, Thesis of Metz University, 2006. “Identification et
Quantification d’une atmosphère gazeuse complexe à l’aide d’un
système multi-capteurs intelligent. Application La détection de mélanges
composés de H2S, NO2, SO2 en atmosphère humide variable”, thèse
d’Université Metz, 2006.
[36] O. Helli, “Gas multi -sensor for the design of an electronic nose for air
pollution monitoring”, Thesis of Metz University, 2003. “Multi-capteurs
de gaz pour la conception d'un nez électronique de surveillance de la
pollution atmosphérique”, thèse d’Université Metz, 2003.
[37] T. Seiyama, N. Yamazoe, S. Yamauchi,Chemical Sensor Technology,
Vol. 1-4, Elsevier Science Ltd, 1988.
[38] Rumelhart, D.E. Hinton, G.E. Williams, “Learning internal
representations by error propagation,” In Rumelhart, D.E. Mc Cleland,
J.L. (Eds.), Parallel Distributed Processing. MIT Press, Cambridge, pp.
318–362, 1986.
[39] W.S. McCulloch, W.H. Pitts, “A logical calculus of the ideas immanent
in nervous activity”, Bulletin of Mathematical Biophysics, vol 5, pp.
115–133, 1943.
[40] Cybenko, “Approximation by superposition of a sigmoidal function, ”
Math. Cont. Sig. Syst 2, pp. 303–314.
[41] S. Ouhmad, H. Roukhe, A, Roukhe “Real time identification of toxic
gases based on artificial neural networks, ” International Journal of
Computational Engineering Research,vol. 04, issue. 4, pp. 67-72, 2014.
@article{"International Journal of Information, Control and Computer Sciences:68801", author = "Slimane Ouhmad and Abdellah Halimi", title = "The Impact of the Number of Neurons in the Hidden Layer on the Performance of MLP Neural Network: Application to the Fast Identification of Toxic Gases", abstract = "In this work, neural networks methods MLP type were
applied to a database from an array of six sensors for the detection of
three toxic gases. The choice of the number of hidden layers and the
weight values are influential on the convergence of the learning
algorithm. We proposed, in this article, a mathematical formula to
determine the optimal number of hidden layers and good weight
values based on the method of back propagation of errors. The results
of this modeling have improved discrimination of these gases and
optimized the computation time. The model presented here has
proven to be an effective application for the fast identification of
toxic gases.
", keywords = "Back-propagation, Computing time, Fast identification, MLP neural network, Number of neurons in the hidden layer.", volume = "8", number = "11", pages = "2060-5", }
