| EPSRC Reference: |
EP/C523903/1 |
| Title: |
Nonlinear dynamics of artificial immune systems |
| Principal Investigator: |
Hone, Professor A |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Department: |
Sch of Maths Statistics & Actuarial Sci |
| Organisation: |
University of Kent |
| Scheme: |
Springboards Scheme (Pre-FEC) |
| Starts: |
01 September 2005 |
Ends: |
31 August 2006 |
Value (£): |
36,026
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| EPSRC Research Topic Classifications: |
| Non-linear Systems Mathematics |
Numerical Analysis |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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With the development of modern electronic computers in the mid-twentieth century, there was a great deal of initial optimism that computers would soon be able to display intelligent behaviour. Computers can now store and process vast amounts of data with superhuman speed. However, on a more practical level, it seems very hard to construct a robot that can perform coordinated movement under autonomous control - a task that animals perform every day. Similarly, the ability to survive and maintain a stable state whilst subjected to the stress of internal disturbances and/or external assaults is a characteristic property of biological organisms known as homeostasis, yet it is difficult to design an artificial system that can achieve this.The realization that biology can inspire useful computational structures has led both to the development of neural networks, based on the connectivity of the neurons in the brain, and to genetic algorithms, based on the Darwinian theory of natural selection. More recently, in the past six years or so, the field of artificial immune systems has emerged as a new way to apply biological metaphors in computer science.In higher organisms, the immune system plays an essential role in maintaining homeostasis in each individual, by recognizing and providing defence against foreign agents that have the potential to cause harm in the body. Artificial immune systems (AIS) have so far been developed in a rather ad hoc way, by abstracting certain immunological processes in order to implement them in the form of computer algorithms. Empirical results show that these algorithms can be extremely effective at solving complex problems, ranging from protein folding in molecular biology to fault detection in electrical engineering. However, due to the novelty of AIS, there is currently no proper theoretical basis for why they work.The aim of this research project is to develop a mathematical framework for analysing AIS, using the theory of nonlinear dynamical systems. Nonlinear systems (as opposed to linear ones) have the property that the whole is greater than the sum of its parts, so that a simple input can lead to a very complicated output. As part of the Fellowship, a statistical analysis of AIS output will be carried out in order to verify and refine the mathematical theory. This will ultimately have great benefits, by providing a rigorous justification for the use of AIS in a variety of applications, including pattern recognition in biological data (bioinformatics), defence and security of computer networks, and control of robotic systems.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.kent.ac.uk |