AU Abstracts 2009 - AU Journal - Assumption University of Thailand

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.
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