AU Abstracts 2009 - AU Journal - Assumption University of Thailand

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An Improved Technique for Retraining Neural Networks in Adaptive Environment Thitipong Tanprasert, Piyabute Fuangkhon and Chularat Tanprasert* Faculty of Science and Technology, Assumption University This paper presents an improved version of the Decay Prior Sampling (DPS) technique for controlling the aging rate of knowledge learned by a multi-layered perceptron type neural network as the network is progressively retrained to handling a new set of training samples in an adaptive environment, in which the new training samples may or may not conform with the learning context of the previous training session. This technique applied the concept of outpost vectors, which loosely define the territory between classes, to represent the domain of previously learned knowledge. The test results confirm the improvement over the original technique that uses only subtracting clustering for modeling such a domain. Keywords: Decay Prior Sampling technique, adaptive learning, incremental learning, multi-layered perceptron Presented at: The 9th International Conference on Intelligent Technologies (Intech 2008), Samui, Surat Thani, Thailand, 7-9 October 2008. Published in: Proc. InTech’08, InTech’08, pp. 77-80. Full paper requisition: ; piyabutefng@au.edu; chularat.tanprasert @nectec.or.th> * Knowledge Elicitation and Archiving Laboratory, National Electronics and Computer Technology Center, Thailand Science Park, Pathum Thani, Thailand. 20
An Incremental Learning Algorithm for Supervised Neural Network with Contour Preserving Classification Piyabute Fuangkhon and Thitipong Tanprasert Faculty of Science and Technology, Assumption University This paper presents an alternative algorithm for integrating the existing knowledge of a supervised learning neural network with the new training data. The algorithm allows the existing knowledge to age out in slow rate as a neural network is gradually retrained with consecutive sets of new samples, resembling the change of application locality under a consistent environment. The algorithm also utilizes the contour preserving classification algorithm to increase the accuracy of classification. The experiment is performed on 2-dimension partition problem and the result convincingly confirms the effectiveness of the algorithm. Keywords: Integrating the existing knowledge, consistent environment, accuracy of classification, 2-dimension partition problem. Presented at: The 6 th International Conference on Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2009), Phatthaya, Chon Buri, Thailand, 6-9 May 2009. Published in: Proc. ECTI-CON 2009, pp. 740-743. Full paper requisition: 21
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AU Abstracts 2009 - AU Journal - Assumption University of Thailand

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