ART-based Neural Networks for Character Recognition
碩士 === 國立成功大學 === 電機工程研究所 === 82 === As nearest neighbor classifiers and multilayer networks with backpropagation algorithm have been widely used for practical pattern recognition，both types of networks have their superior and unique...
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| Format: | Others |
| Language: | en_US |
| Published: |
1994
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| Online Access: | http://ndltd.ncl.edu.tw/handle/52986108434092270999 |
| Summary: | 碩士 === 國立成功大學 === 電機工程研究所 === 82 === As nearest neighbor classifiers and multilayer networks with
backpropagation algorithm have been widely used for
practical pattern recognition，both types of networks have
their superior and unique characteristics．Therefore，it has
been proposed to combine the superior coming from these two
types of networks， so to enhance the performance of the
nearest neighbor classifier through iterative backpropagation
training．The network is called nearest neighbor isomophic
network (NNIN)．However， as using the NNIN for pattern
recognition，one problem still exists, that is the
determination of the number of clusters within each class．
In practical applications， it is common to find that the
patterns of each class distribute differently．Some class
has a unique high density distribution． Some class
distribute sparely，or separated into a few groups. Hence
some way to allow the network automatically construc the
required network architecture provides the applications
robustness． In this paper，the ART networks are slighty
modified to incorporate the Mahalanobis distance in order
consider the different pattern deviations in various
directions．The modified ART are used in the front layers
of the nearest neighbor classifier to robustly construct
the optimal cluster neurons．The proposed network was
extensively tested by hand-written digits． Simulation
results indicate that this network combining the adaptive
ART networks， the concept of nearest neighbor classifier
andf the modified backpropagation learning rule provides a
high accurate classification capability with a fast learning
rate．
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