Main Article Content

Abstract

Artificial Neural Networks (ANNs) are computer software programs that mimic the human brain's ability to classify patterns or to make forecasts or decisions based on past experience.  The development of this research area can be attributed to two factors, sufficient computer power to begin practical ANN-based research in the late 1970s and the development of back-propagation in 1986 that enabled ANN models to solve everyday business, scientific, and industrial problems.  Since then, significant applications have been implemented in several fields of study, and many useful intelligent applications and systems have been developed.  The objective of this paper is to generate awareness and to encourage applications development using artificial intelligence-based systems.  Therefore, this paper provides basic ANN concepts, outlines steps used for ANN model development, and lists examples of engineering applications based on the use of the back-propagation paradigm conducted in Oman.  The paper is intended to provide guidelines and necessary references and resources for novice individuals interested in conducting research in engineering or other fields of study using back-propagation artificial neural networks.    

 

 

Keywords

Artificial Neural Network Applications Engineering Problems Back-Propagation Forecasting Classification Oman.

Article Details

References

  1. AL-ALAWI, S.M. and AL-HINAI, H. 1998. An ANN based approach for predicting global radiation in locations with no direct measurement instrumentation. The Sixth Arab International Solar Energy Conference (ISEC 6), March 29-April 1, 1998, Sultanate of Oman.
  2. AL-ALAWI, S.M. and AL-HINAI, H. 1998. An ANN based approach for predicting global radiation in locations with no direct measurement instrumentation”. Renewable Energy Journal. Elsevier Science, U.K., 14: 199-204.
  3. AL-ALAWI, S.M. and AL-HINAI, H.A. 1996. Analysis and Prediction of Clearness Index Using Artificial Neural Networks, Proceedings of the World Renewable Energy Congress, 3: 2115-2119, June 1996, Colorado, U.S.A.
  4. AL-ALAWI, S.M. and AL-HINAI, H.A. 1996. Analysis and Prediction of Clearness Index Using Artificial Neural Networks. Renewable Energy Journal. 3: 2115-2119.
  5. AL-ALAWI, S.M. and ALI, G.A. 1996. A Novel Approach for Traffic Accident Analysis and Prediction Using Artificial Neural Networks, Road & Transport Research Journal, 5: 118-128.
  6. AL-ALAWI, S.M., BENJAMIN, C.O., and OMURTAG, Y. 1992. Intelligent Monitoring and Control of Large Engineering Projects, 6th Oklahoma Symposium on Artificial Intelligence, November 11-12, 1992, pp. 115-124, Tulsa, Oklahoma, U.S.A.
  7. AL-ALAWI, S.M., BOUKADI, F.H., and BEMANI, A.S. 1998. Matrix and Cement Effects on Residual Oil Saturation in Sandstone Formations: A Neural Network Approach, the Journal of Petroleum Science & Technology, SQU, January 24, 1998.
  8. AL-ALAWI, S.M. and ELLITHY, K.A. 2000. Tuning of SVC Damping Controllers Over Wide Range of Load Models Using Artificial Neural Network. Electrical Power & Energy Systems Journal, 22: 405-420.
  9. AL-ALAWI, S.M. and ISLAM, S.M. 1995. Short-Term Load Forecasting Using Artificial Neural Networks. 2nd IEEE International Conference on Electronics, Circuits and Systems (ICECS'95). December 17-21, 1995. Amman, Jordan. pp.381-384.
  10. AL-ALAWI, S.M., JERVASE, J.A., and JAWAD, A.M. 1998. Statistical Signal Characterization – Artificial Neural Network Based Hybrid System for Electrocardiogram Interpretation”. Conference on Computational Aspects and their Applications in Electrical Engineering, July 22-23, 1998, Amman, Jordan.
  11. AL-ALAWI, S.M., KALAM, M.Z., and AL-MUKHEINI, M. 1996. Application of ANN to Predict Wettability and Relative Permeability of Sandstone Rocks, Engineering Journal of Qatar University, 9: 29-43.
  12. AL-ALAWI, S.M., SEIBI, A.C. and AL-ORAIMI, S.K. 1996. Prediction of Failure Mechanisms and Mechanical Properties of Fastened GRP Under Bending Using Artificial Neural Networks, Proceedings of the First International Conference on Composite Science and Technology, pp. 7-12, June 18-20, 1996, Durban, South Africa.
  13. AL-ALAWI, S.M., SIDDIQUI, R.A., and ALBALUSHI, K. 1997. Artificial Neural Networks: A Novel Approach for the Analysis and Prediction of Mechanical Properties of 6063 Aluminum Alloy, Al-Azhar Engineering International Conference in Cairo, Egypt, September 1997.
  14. AL-ALAWI, S.M. and TAWO, E.E. 1998. Application of Artificial Neural Networks in Mineral Resource Evaluation”, Journal of King Saud University for Engineering Sciences, 10: 127-139.
  15. ALI, G.A., AL-ALAWI, S.M., and BAKHEIT, C.S. 1998. A Comparative Analysis and Prediction of Traffic Accident Casualties in the Sultanate of Oman Using Artificial Neural Networks and Statistical Methods, SQU Journal for Science and Technology, 3: 11-20.
  16. ANDERSON, J.A. 1990. Data Representation in Neural Networks. AI Expert, June, pp. 30-37.
  17. BAILEY, D. and THOMPSON, D. 1990. How to Develop Neural Networks. AI Expert, June, pp. 38-47.
  18. BARAKAT E.H. and AL RASHAD, S.A. 1993. Social environmental and economic constraints affecting power and energy requirements in fast developing areas, Power Eng. J., 7(4): 177-184.
  19. BOUKADI, F. H. and AL-ALAWI, S.M. 1998. Matrix and Cement Effects on Residual Oil Saturation in Sandstone Formations”, Petroleum Science and Technology Journal, U.S.A., 17: 99-113.
  20. BOUKADI, F. and AL-ALAWI, S.M. 1997. Analysis and Prediction of Oil Recovery Efficiency in Limestone Cores Using Artificial Neural Networks, Energy & Fuels Journal, U.S.A., 11: 1056-1060.
  21. BOUKADI, F., AL-ALAWI, S.M., AL-BEMANI, A. and AL-QASSABI, S. Establishing PVT Correlations for Omani Oils”, Petroleum Science and Technology, U.S.A., September 1998.
  22. BUNN, D.W. and FARMER, E.D. 1985. Comparative Models for Electrical Load Forecasting, John Wiley & Sons.
  23. BURDEN, R.L. and FAIRIES, J.D. 1985. Numerical Analysis, Prindel, Webber, and Schmidt.
  24. ELLITHY, K.A., AL-ALAWI, S.M., and ZAINALABDEEN, H.M. 1997. Tuning Power System Stabilizers over a Wide Range of Load Models Using Artificial Neural Networks”, Journal of Engineering and Applied Science, Cairo University, 44: 389-406.
  25. GASTLI, A., AKHERRAZ, M. and AL-ALAWI, S.M. 2000. ANN-Based Load Identification and Control of AC Voltage Regulator. Proceedings of the IEEE International Energy Conference (IEC 2000), Al-Ain, UAE, May 7-9, 2000, CD-ROM.
  26. ISLAM, S.M. and AL-ALAWI, S.M. 1995. Forecasting Long-Term Electrical Peak Load and Energy Consumption for a Fast Growing Utility Using Artificial Neural Networks, Proceedings of the IEE International Power Engineering Conference (IPEC’95), Singapore, March 1995, pp. 690-695.
  27. ISLAM, S.M., AL-ALAWI, S.M., and ELLITHY, K.A. 1995. Forecasting Monthly Electrical Load and Energy for a Fast Growing Utility Using an Artificial Neural Network. Electric Power Systems Research Journal, 34: 1-9.
  28. ISLAM, M., AL-ALAWI, S.M. and LEDWICH, G. 1996. On-Line Unit Commitment for a Generation Constrained Fast Growing Utility Using Artificial Neural Networks, The Australian Universities Power Engineering Conference (AUPEC’96), October 2-4, 1996, Melbourne, Australia.
  29. KALAM, M.Z., AL-ALAWI, S.M., and AL-MUKHEINI, M. 1996. Assessment of Formation Damage Using Artificial Neural Networks, SPE Paper #31100, Proceedings of the International Symposium on Formation Damage Control, pp. 301-309, February 14-15, 1996, Lafayette, Louisiana, U.S.A.
  30. KALAM, M.Z., AL-ALAWI, S.M. and AL-MUKHEINI, M. 1995. The Application of Artificial Neural Networks to Reservoir Engineering, IATMI International Symposium on Production Optimization, July 22-26, 1995, pp. 1-8, Bandung, Indonesia.
  31. LAWRENCE, J. 1991. Data Preparation for a Neural Network. AI Expert, November, pp. 34-41.
  32. LUQMAN, A. and AL-ALAWI, S.M. 2000. Forecasting Fish Exports in the Sultanate of Oman Using Artificial Neural Networks, Jordanian Journal “Derasat” , 22(2): .
  33. MENDENHALL, W. and BEAVER, R.J. 1994. Introduction to Probability and Statistics, Duxbury Press.
  34. MYHARA, R.M., SABLANI, S.S., AL-ALAWI, S.M. and TAYLOR, M.S. 1998. Water Sorption Isotherms of Dates: Modeling Using GAB Equation and Artificial Neural Network Applications, Lebensmittel Wissenchaft und Technologie (German Journal), Food Science and Technology (English name), 33: 699-706.
  35. RUMELHART, D., MCCLELLAND, J., and PDP RESEARCH GROUP. 1988. Parallel Distributed Processing, Explorations in the Microstructure of Cognition. 11; Foundations. Cambridge, MA. MIT Press/Bradford Books.
  36. SCHUNIKER, O., AL-ALAWI, S.M., AL-BEMANI, A.S., and KALAM, M.Z. 1999. Preliminary Studies on Using Artificial Neural Networks to Predict Sedimentary Facies of the Petro-Carboniferous Glacigenic Al Khlata Formation in Oman, 11th SPE Middle East Oil Show & Conference (MEOS’99), February 20-23, 1999, Bahrain.
  37. SEIBI, A. and AL-ALAWI, S.M. 1999. Experimental Investigation and Failure Analysis of Fastened GRP Under Bending Using the Finite Element Method and Artificial Neural Network Modeling, the Journal of Science & Technology, 4: 71-78.
  38. SEIBI, A.C. and AL-ALAWI, S.M. 1999. Design of Fiberglass/Copper Moulds Using Finite Element Analysis. First International Conference on Composite Science and Technology, Orlando, Florida, June 1999.
  39. SEIBI, A. and AL-ALAWI, S.M. 1997. Prediction of Fracture Toughness Using Artificial Neural Networks. Engineering Fracture Mechanics Journal, 56: 311-319.
  40. SEIBI, A.C., AL-ORAIMI, S.K., and AL-ALAWI, S.M. 1996. Effects of Joint Geometry on the Flexural Behaviour of Glass Reinforced Plastics, Proceedings of the First International Conference on Composite Science and Technology, pp. 471-476, June 18-20, 1996, Durban, South Africa.
  41. SIMPSON, P.K. 1990. Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations. (1st Edition). Pergamon Press, Inc., Elmsford, NY.
  42. STANLEY, J. 1990. Introduction to Neural Networks. (3rd Edition). Sierra Madre.
  43. TAWO, E.E. and AL-ALAWI, S.M. 1999. A Comparison between an Artificial Neural Network and Geostatistical Technique in the Estimation of Regionalized Variables, Engineering Journal of Qatar University , 12:125-149.
  44. WARD SYSTEMS GROUP, INC. 1991. NeuroShell, Neural Network Shell Program. (4th Edition). Frederick, MD.