You are here

Home » Content

GENETIC CELLULAR NEURAL NETWORK APPLICATIONS FOR PREDICTION PURPOSES IN INDUSTRY

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2003

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Genetic Cellular Neural Networks (GCNN) are adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. It was argued that due to the learning ability of neural networks, their speed and capacity to handle large amount of data, they have a potential for predicting components requirements. GCNN use less stability parameters than Back Propagation-Artificial Neural Networks (BP-ANN) and hence better suited to fast changing scenarios as in real supply chain applications. The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-genetic-cellular-neural-network-applications-prediction-purposes-industry.pdf
  • 1
683-691
  • English

REFERENCES

References: 

[1] Ziarati, M. and Ucan, O.N., “Optimisation of Economic Order Quantity Using Neural Networks Approach”, Dogus University Journal Number, No: 3, pp.128-140, January 2001
[2] Wang, Q., “Improving the Cost Model Development Process Using Neural Networks”, Thesis, De Monfort University, November 2000
[3] Stockton, D.T., Quinn, L., “Identifying Economic Order Quantities Using Genetic Algorithms” International Journal of Operations and Production Management, V3, n11, 1993.
[4] Ucan, O. et al., “Separation of Bouguer anomaly map using cellular neural network”, Journal of Applied Geophysics 46, pp.129-142 , 2001.
[5] Ziarati, R., Khataee, A., “Integrated Business Information System (IBIS) – A Quality Led Approach”, Keynote Address. SheMet 94, Belfast University Press, Ulster, UK, April 1994.
[6] Ziarati, R., “Factories of the Future”, Invited paper, EUROTECNET Conference, Germany
[7] Chua, L. O. and Yang, L., “Cellular Neural Networks: Theory”, IEEE Trans. Circuit and Systems, V35, pp.1257-1272, 1998.
[8] Kozek, T., Roska, T. and Chua, L. O., “Genetic Algorithms for CNN template Learning”, IEEE Trans. Circuit and Systems, V40, pp.392-402, 1988.
[9] Davis, L., “Handbook of Genetic Algorithms” New York: Van Nostrand Reinhold., 1991.
[10] Holland, J. H., , “Outline for a logical theory of adaptive systems: J. Assoc.” Computer V3, pp.297-314, 1975.
[11] Holland, J. H., “Adaptation in neural and artificial systems” Ann Arbor, MI: University of the Michigan Press, 1975.
[12] A.Muhittin Albora, Atilla Ozmen, Osman N. Ucan,"Residual Separation of magnetic fields using a Cellular Neural Network Approach", Pure Applied Geophysics, 158, pp. 1797-1818, Sept., 2001.
[13] A.Muhittin Albora, Osman N. Ucan, Atilla Ozmen, Tulay Ozkan, "Separation of Bouguer anomaly map using cellular neural network", Journal of Applied Geophysics, 46, pp.129-142, 2001.
[14] Adem Karahoca, Osman N. Ucan, Erkan Danacý,"Random Neural Network Approach in Distributed Database Management System", Journal of Electrical &Electronics, Vol.1, Number 1, pp,84-110, 2001.
[15] Osman N. Ucan, A. Özmen," Performance of Gray Scaled Images Using Segmented Cellular Neural Network Combined Trellis coded Quantization/Modulation (SCNN-CNN CTCQ/TCM) Approach over Rician Fading Channel", Dogus University Journal, pp.217- 224, January 2000.
[16] Osman N. Ucan, S. Seker and S. Paker, "Jitter Performance of Neural Network Equivalent MPSK Schemes Over Microwave Channels", International Journal of Communication Systems, Vol 11, pp. 169-178 May-June 1998.
[17] Osman N. Ucan, Murat Uysal and Atilla Ozmen ," Combined TCQ/TCM and Neural Network Modelling (In Turkish) " Electrical, Electronics and Computer Technologies Conf. Adana, pp.35, 1998.
[18] Osman N. Ucan, A. Özmen," Performance of Gray Scaled Images using Quantized Cellular Neural Network Combined Trellis coded Quantization/Modulation (QCNN-CNN CTCQ/TCM) Approach over Rician Fading Channel", International Conference on Telecommunications, June 15-18 Kore, 1999
[19] Osman N. Ucan, A. Özmen" Performance of Cellular Neural Network modeled trellis coded quantization/modulation signals at Rician channels," IEEE Signal processing and Applications , Turkey, 1999.
[20] Baran Tander, Osman N. Ucan," 3x3 stable cellular neural network model using PSPICE programming", Electrical Engineering Chamber , Gaziantep, 1999.
[21] Mukden Uður, Osman N. Ucan, Ayten Kuntman, Atilla Özmen, Ahmet Merev, " Analysing the 2-D surface tracking patterns by using cellular neural networks", IEEE Int. Power Conf., USA. 1999

Thank you for copying data from http://www.arastirmax.com