Araştırma Makalesi
BibTex RIS Kaynak Göster

Forecasting of Zinc Coating Thickness with Artificial Neural Network

Yıl 2013, Cilt: 17 Sayı: 1, 61 - 69, 01.04.2013

Öz

Since the competition level among the companies is increasing day by day, meeting customer demands with qualified products and cost reduction are primary goals of each company. And zinc, the main raw material in galvanization sector, is the most important cost item. So it is required to forecast the amount of zinc to be spent. In this study it is tried to forecast the amount of zinc consumption using the artificial neural network (ANN) method. To evaluate the convenience of values hypothesis tests are done; and the results showed that there is no significant difference between the predicted and real outputs statistically

Kaynakça

  • ZHANG,Y.F.,Fuh,J.Y.H., A Neural Network Approach For Early Cost Estimation Of Packaging Products, Computers ind.Engng, 34(2), 433-450, 1998.
  • KERMANSHAHİ, B., IWAMİYA, H., Up to year 2020 load forecasting using neural nets, Electrical power and energy systems, 24, 789- 797, 2002.
  • CAVALİERİ,S., PİNTO,R., networkmodels production costs: A case study in the automotive Economics, 91, 165-177, 2004. vs. estimation for the of industry, Int. J. Production
  • WANG, Q., Artificial neural networks as cost engineering methods in a collaborative manufacturing environment , Int. J. Production Economics, 109, 53-64, 2007.
  • GUNAYDIN ,H.M., DOGAN ,S.Z., A neural network approach for early cost estimation of structural systems of buildings, International Journal of Project Management, 22, 595–602, 2004.
  • PARAG C. PENDHARKAR, Scale economies and production function estimation for object- oriented software component and source code documentation size, European Journal of Operational Research, 172, 1040–1050, 2006.
  • PİNO, R., PARRENO, J., GOMEZ, A., Priore, P., Forecasting next-day price of electricity in the Spanish energy market using artificial neural Networks, Engineering Applications of Artificial Intelligence, 21, 53-62, 2008.
  • CAPUTO , A.C., PELAGAGGE, P.M., Parametric and neural methods for cost estimation of process vessels, Int. J. Production Economics, 112, 934–954, 2008.
  • VERLINDEN, B.,Duflou, J.R., Collin,P., Cattrysse,D., Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study, Int. J. Production Economics, 111, 484–492, 2008.
  • RESKOVIC S., GLAVAS Z., The Applıcatıon Of An Artıfıcıal Neural Network For Determınıng The Influence Of The Parameters For The Deposıtıon Of A Zınc Coatıng On Steel Tubes, Materıalı In Tehnologıje, 43(4), 201-205, 2009.
  • ÖZTEMEL, E., Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul, 2006. [12] HAYKIN, S., Neural Foundation, a Comprehensive College Publishing, New Jersey, 1999. MacMillan
  • FAUSETT, L., Fundamentals of Neural Networks Architectures, Algorithms, and Applications, Prentice-Hall, New Jersey, 1994.
  • NABIYEV, V., Yapay Zeka Problemler- Yöntemler-Algoritma, Ankara, 2005. Seçkin Yayıncılık,
  • TAEHO Jo, The effect of mid-term estimation on back propagation for time series prediction, Neural Computing and Applications, 19, 1237- 1250, 2010.
  • GRAUPE, D., Principles of Artificial Neural Networks (2ndEdition),World Scientific, 2007.
  • BADRI, A., AMELI, Z., BIRJANDI, A.M., Application of Artificial Neural Networks and Fuzzy logic Methods for Short Term Load Forecasting, Energy Procedia, 14, 1883–1888, 2012.
  • GUO, Z., ZHAO, W., LU, H., WANG, J., Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model,Renewable Energy, 37(1), 241–249, 2012.
  • ABHISHEK, K., SINGHA, M.P., GHOSH, S., ANAND, A., Weather forecasting model using Artificial Technology 4, 311 – 318, 2212-0173, 2012.
  • DEHURI, S., ROY, R., CHO, S., Ghosh, A. ,An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification, The Journal of Systems and Software, 85, 1333– 1345, 2012.
  • EL-MıDANY , T.T., EL-BAZ , M.A., ABD- ELWAHED, M.S., A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks Expert Systems with Applications, 37, 1035–104, 2010.

Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi

Yıl 2013, Cilt: 17 Sayı: 1, 61 - 69, 01.04.2013

Öz

Since the competition level among the companies is increasing day by day, meeting customer demands with qualified products and cost reduction are primary goals of each company. And zinc, the main raw material in galvanization sector, is the most important cost item. So it is required to forecast the amount of zinc to be spent. In this study it is tried to forecast the amount of zinc consumption using the artificial neural network (ANN) method. To evaluate the convenience of values hypothesis tests are done; and the results showed that there is no significant difference between the predicted and real outputs statistically.

Kaynakça

  • ZHANG,Y.F.,Fuh,J.Y.H., A Neural Network Approach For Early Cost Estimation Of Packaging Products, Computers ind.Engng, 34(2), 433-450, 1998.
  • KERMANSHAHİ, B., IWAMİYA, H., Up to year 2020 load forecasting using neural nets, Electrical power and energy systems, 24, 789- 797, 2002.
  • CAVALİERİ,S., PİNTO,R., networkmodels production costs: A case study in the automotive Economics, 91, 165-177, 2004. vs. estimation for the of industry, Int. J. Production
  • WANG, Q., Artificial neural networks as cost engineering methods in a collaborative manufacturing environment , Int. J. Production Economics, 109, 53-64, 2007.
  • GUNAYDIN ,H.M., DOGAN ,S.Z., A neural network approach for early cost estimation of structural systems of buildings, International Journal of Project Management, 22, 595–602, 2004.
  • PARAG C. PENDHARKAR, Scale economies and production function estimation for object- oriented software component and source code documentation size, European Journal of Operational Research, 172, 1040–1050, 2006.
  • PİNO, R., PARRENO, J., GOMEZ, A., Priore, P., Forecasting next-day price of electricity in the Spanish energy market using artificial neural Networks, Engineering Applications of Artificial Intelligence, 21, 53-62, 2008.
  • CAPUTO , A.C., PELAGAGGE, P.M., Parametric and neural methods for cost estimation of process vessels, Int. J. Production Economics, 112, 934–954, 2008.
  • VERLINDEN, B.,Duflou, J.R., Collin,P., Cattrysse,D., Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study, Int. J. Production Economics, 111, 484–492, 2008.
  • RESKOVIC S., GLAVAS Z., The Applıcatıon Of An Artıfıcıal Neural Network For Determınıng The Influence Of The Parameters For The Deposıtıon Of A Zınc Coatıng On Steel Tubes, Materıalı In Tehnologıje, 43(4), 201-205, 2009.
  • ÖZTEMEL, E., Yapay Sinir Ağları, Papatya Yayıncılık, İstanbul, 2006. [12] HAYKIN, S., Neural Foundation, a Comprehensive College Publishing, New Jersey, 1999. MacMillan
  • FAUSETT, L., Fundamentals of Neural Networks Architectures, Algorithms, and Applications, Prentice-Hall, New Jersey, 1994.
  • NABIYEV, V., Yapay Zeka Problemler- Yöntemler-Algoritma, Ankara, 2005. Seçkin Yayıncılık,
  • TAEHO Jo, The effect of mid-term estimation on back propagation for time series prediction, Neural Computing and Applications, 19, 1237- 1250, 2010.
  • GRAUPE, D., Principles of Artificial Neural Networks (2ndEdition),World Scientific, 2007.
  • BADRI, A., AMELI, Z., BIRJANDI, A.M., Application of Artificial Neural Networks and Fuzzy logic Methods for Short Term Load Forecasting, Energy Procedia, 14, 1883–1888, 2012.
  • GUO, Z., ZHAO, W., LU, H., WANG, J., Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model,Renewable Energy, 37(1), 241–249, 2012.
  • ABHISHEK, K., SINGHA, M.P., GHOSH, S., ANAND, A., Weather forecasting model using Artificial Technology 4, 311 – 318, 2212-0173, 2012.
  • DEHURI, S., ROY, R., CHO, S., Ghosh, A. ,An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification, The Journal of Systems and Software, 85, 1333– 1345, 2012.
  • EL-MıDANY , T.T., EL-BAZ , M.A., ABD- ELWAHED, M.S., A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks Expert Systems with Applications, 37, 1035–104, 2010.
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Tuğçen Hatipoğlu Bu kişi benim

Semra Boran Bu kişi benim

Burcu Özcan Bu kişi benim

Alpaslan Fığlalı Bu kişi benim

Yayımlanma Tarihi 1 Nisan 2013
Gönderilme Tarihi 3 Ekim 2012
Kabul Tarihi 24 Aralık 2012
Yayımlandığı Sayı Yıl 2013 Cilt: 17 Sayı: 1

Kaynak Göster

APA Hatipoğlu, T., Boran, S., Özcan, B., Fığlalı, A. (2013). Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi. Sakarya University Journal of Science, 17(1), 61-69. https://doi.org/10.16984/saufbed.59229
AMA Hatipoğlu T, Boran S, Özcan B, Fığlalı A. Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi. SAUJS. Nisan 2013;17(1):61-69. doi:10.16984/saufbed.59229
Chicago Hatipoğlu, Tuğçen, Semra Boran, Burcu Özcan, ve Alpaslan Fığlalı. “Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi”. Sakarya University Journal of Science 17, sy. 1 (Nisan 2013): 61-69. https://doi.org/10.16984/saufbed.59229.
EndNote Hatipoğlu T, Boran S, Özcan B, Fığlalı A (01 Nisan 2013) Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi. Sakarya University Journal of Science 17 1 61–69.
IEEE T. Hatipoğlu, S. Boran, B. Özcan, ve A. Fığlalı, “Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi”, SAUJS, c. 17, sy. 1, ss. 61–69, 2013, doi: 10.16984/saufbed.59229.
ISNAD Hatipoğlu, Tuğçen vd. “Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi”. Sakarya University Journal of Science 17/1 (Nisan 2013), 61-69. https://doi.org/10.16984/saufbed.59229.
JAMA Hatipoğlu T, Boran S, Özcan B, Fığlalı A. Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi. SAUJS. 2013;17:61–69.
MLA Hatipoğlu, Tuğçen vd. “Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi”. Sakarya University Journal of Science, c. 17, sy. 1, 2013, ss. 61-69, doi:10.16984/saufbed.59229.
Vancouver Hatipoğlu T, Boran S, Özcan B, Fığlalı A. Yapay Sinir Ağı Yaklaşımıyla Çinko Kalınlığının Tahminlenmesi. SAUJS. 2013;17(1):61-9.