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Biologically inspired computing that looks to nature and biology for inspiration is a revolutionary change to our thinking about solving complex computational problems. It looks into nature and biology for inspiration rather than conventional approaches. The Human Immune System with its complex structure and the capability of performing pattern recognition, self-learning, immune-memory, generation of diversity, noise tolerance, variability, distributed detection and optimisation - is one area that has been of strong interest and inspiration for the last decade. An air conditioning system is one example where immune principles can be applied. This paper describes new computational technique called Artificial Immune System that is based on immune principles and refined for solving engineering problems. The presented system solution applies AIS algorithms to monitor environmental variables in order to determine how best to reach the desired temperature, learn usage patterns and predict usage needs. The aim of this paper is to explore the AIS-based artificial intelligence approach and its impact on energy efficiency. It will examine, if AIS algorithms can be integrated within a Smart Air Conditioning System as well as analyse the impact of such a solution.
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Tom
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193--199
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Bibliogr. 14 poz., wykr.
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autor
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- Faculty of Engineering and IT, University of Technology, Sydney, Bld 1 Level 22 Broadway, Ultimo 2007, NSW, Australia, zenon.chaczko@uts.edu.au
Bibliografia
- [1] D. R. Barr, G. K. Poock, and F. R. Richards, Experimentation Manual Part 1: Experimentation Methodology. Monterey, California: Naval Postgraduate School, 1978.
- [2] C. Charras and T. Lecroq, “Exact string matching algorithms,” 1997, retrieved 13/02/12 from http://www-igm.univ-mlv.fr/lecroq/string/.
- [3] C. A. C. Coello, D. C. Rivera, and N. C. Cortes, “Use of an artificial immune system for job scheduling,” in Artificial Immune System, J. Timmis, P. Bently, and E. Hart, Eds. Berlin: Springer-Verlag, 2003, pp. 1-10.
- [4] D. Dasgupta, “Advances in artificial immune systems,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 40-49, 2006.
- [5] L. N. de Castro and F. J. von Zuben, “aiNET: An artificial immune network for data analysis,” in Data Mining: A heuristic approach, H. A. Abbass, R. A. Sarker, and C. Newton, Eds. Idea Group Publishing, 2001, pp. 231-259.
- [6] M. C. Mozer, “12 lessons from an adaptive house,” in Smart environments: Technologies, protocols, and applications, D. Cook and R. Das, Eds. J. Wiley & Sons, 2005, pp. 273-294.
- [7] M. Lehmann and W. Dilger, “Controlling the heating system of an intelligent home with an artificial immune system,” in International Conference on Artificial Immune Systems, H. Bersini and J. Carneiro, Eds. Berlin: Springer-Verlag, 2006, pp. 335-348.
- [8] E. Bendiab, “Artificial immune system for multimodality image alignment,” in Artificial Immune System, J. Timmis, P. Bently, and E. Hart, Eds. Berlin: Springer-Verlag, 2003, pp. 11-19.
- [9] L. N. de Castro and J. Timmis, Artificial Immune Systems: A Computational Intelligence Approach. London: Springer-Verlag, 2002.
- [10] L. N. de Castro and F. J. von Zuben, Recent Developments in Biological Inspired Computing. Hershey: Idea Group Publishing, 2005.
- [11] D. Dumitrescu, B. Lazzerini, L. Jain, and A. Dumitrescu, Evolutionary Computation. USA: CRC Press, 2000.
- [12] D. Graupe, Principles of Artificial Neural Networks. Singapore: Word Scientific Publishing, 1997.
- [13] C. A. Janeway Jr, P. Travers, M. Walport, and M. Shlomchik, Immunobiology: the Immune System in Health and Disease, 5th ed. New York: Garland Publishing, 2001.
- [14] M. Middlemiss, Framework for intrusion detection inspired by the immune system. University of Otago, 2005, Discussion Paper.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA1-0053-0026