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Title: Heuristic pattern correction scheme using adaptively trained generalized regression neural networks
Authors: Hoya, Tetsuya
Chambers, Jonathon
Keywords: Generalized regression neural networks (GRNNs)
Incremental learning
Pattern classification
Pattern correction
Issue Date: 2001
Publisher: © IEEE
Citation: HOYA, T. and CHAMBERS, J.A., 2001. Heuristic pattern correction scheme using adaptively trained generalized regression neural networks. IEEE Transactions on Neural Neworks, 21(1), pp. 91 - 100
Abstract: In many pattern classification problems, an intelligent neural system is required which can learn the newly encountered but misclassified patterns incrementally, while keeping a good classification performance over the past patterns stored in the network. In the paper, an heuristic pattern correction scheme is proposed using adaptively trained generalized regression neural networks (GRNNs). The scheme is based upon both network growing and dual-stage shrinking mechanisms. In the network growing phase, a subset of the misclassified patterns in each incoming data set is iteratively added into the network until all the patterns in the incoming data set are classified correctly. Then, the redundancy in the growing phase is removed in the dual-stage network shrinking. Both long- and short-term memory models are considered in the network shrinking, which are motivated from biological study of the brain. The learning capability of the proposed scheme is investigated through extensive simulation studies
Description: This article was published in the journal, IEEE Transactions on Neural Networks [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Version: Published
DOI: 10.1109/72.896798
URI: https://dspace.lboro.ac.uk/2134/5790
ISSN: 1045-9227
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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