Wu, Q, McGinnity, TM, Prasad, G and Bell, D (2005) Evolving learning mechanism for a general computing network model. In: 2005 IEEE International Conference on Systems, Man, and Cybernetics, Hawaii, USA. IEEE. 6 pp. [Conference contribution]
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In this paper, an evolving learning mechanism is proposed for general computing network model to make decisions in intelligent systems. The novel mechanism is performed by means of a set of computing cell operations such as self-generation, growth, self-division, and death. Under the mechanism, a computing network grows up to a mature network. A hidden cell in the network is defined as a condition matching-unit in response to a fuzzy sub-superspace in multiple-dimension input superspace. A sense-function is defined to represent connections from a hidden cell to input cells. The range and edge vagueness of the sense-function are determined by evolving learning mechanism when sample instances are presented to the network. This network is able to learn from a very few training instances to make decisions for unseen instances. The benchmark data sets from the UCI machine learning repository are applied to test the network and comparable results are obtained.
|Item Type:||Conference contribution (Paper)|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
|Research Institutes and Groups:||Computer Science Research Institute > Intelligent Systems Research Centre|
Computer Science Research Institute
|Deposited By:||Professor Girijesh Prasad|
|Deposited On:||16 May 2011 10:42|
|Last Modified:||16 May 2011 10:42|
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