Abstract

Intelligent control denotes the capacity to acquire and apply knowledge in control process. The important characteristics of intelligent control systems are information abstraction and knowledge-based decision making. There are different control paradigms available in the literature including Artificial Neural Networks, Fuzzy Logic Systems, Genetic Algorithms, Hybrid Models and others. This paper attempts to design open loop controller using Self Organizing Map and studies its nature and accuracy with an example.

References

• Karray, F. O. , and Silva, C. D. 2004. Soft Computing and Intelligent Systems Design Theory, Tools and Applications. Pearson Education.
• Passino, K. M. 2001. Intelligent Control: An Overview of Techniques, Chapter in: T. Samad, Ed. , "Perspectives in Control: New Concepts and Applications", IEEE Press, NJ.
• Antsaklis, P. J. 1999. Intelligent Control, Encyclopedia of Electrical and Electronics Engineering, 10, 493-503, John Wiley & Sons, Inc.
• Sathya, R, and Manohar, G. T. 2008. Introducing Intelligent Control Paradigms to Potential Researchers. J. Jyoti Research Academy. 2 (Dec 2008), 7-12.
• Kohonen, T. 1998. Neurocomputing. 21, Issue 1-3.
• LiMin Fu. 2003. Neural Networks in Computer Intelligence. Tata McGraw-Hill Publishing Company Limited.
• Herbst, M. , Gupta, H. V. , and Casper, M. C. 2009. Mapping Model Behaviour using Self-Organizing Maps. Hydrol. Earth Syst. Sci. , 13, 395–409.
• Aroui, T. , Koubaa, Y. , and Toumi, A. 2009. Clustering of the Self-Organizing Map based Approach in Induction. Machine Rotor Faults Diagnostics. Leonardo Journal of Sciences, 15, 1-14.
• Sathya, R. , and Abhraham, A. 2010. Application of Kohonan SOM in Prediction. In the Proceeding of ICT conference. CCIS 101. Springer-Verlag Berlin Heidelberg, 313–318.
• Asif, U. K. , Bandopadhyaya, T. K. , and Sudhir, S. 2009. Classification of Stocks Using Self Organizing Map. J. Soft Computing Applications. 4, 19-24.
• Andrews, R. , and Geva, S. 2000. Rule Extraction From Local Cluster Neural Nets. Neurocomputing.
• Steiner, M. T. A. , Neto, P. J. S. , Soma, N. Y. , Shimizu, T. , and Nievola, J. C. 2006. Using Neural Network Rule Extraction for Credit-Risk Evaluation. J. IJCSNS, 6 (5A).
• Kamruzzaman, S. M. , and Md. , M. , Islam. 2006. An Algorithm to Extract Rules from Artificial Neural Networks for Medical Diagnosis Problems. Int. J. Information Technology, 12 (8).
• Sathya, R. , and Abraham, A. 2012. Rule Extraction from SOM for Academic Evaluation, In the Proceedings of ICACII, Lecture Notes in Information Technology. 10, 184-189.
• Naganathan, E. R, Venkatesh, R. , and Uma Maheswari. 2008. Intelligent Tutoring System: Predicting Students Results Using Neural Networks. J. Convergence Information Technology, 3(3), 22-26.
• Karamouzis, S. T. , and Vrettos, A. 2008. An Artificial Neural Network for Predicting Student Graduation Outcomes. In the Proceedings of the WCECS 2008.
• Oladokun, V. O. , Adebanjo, A. T. , and Charles-Owaba, O. E. 2008. Predicting Students' Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course. The Pacific Journal of Science and Technology, 9, 72–79.