Thammano, Arit
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Thammano, Arit
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Thammano, A.
Thummano, Arit
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arit.th@kmitl.ac.th
4 results
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Item type:Publication, Printed Thai character recognition using the hybrid approach(2002-01-01); Ruxpakawong, PhongthepMany researchers have been conducted on the recognition of Thai characters. Different approaches, such as neural network, syntactic, and structural methods, have been proposed. However, the success in recognizing Thai characters is still limited, compared to English characters. This paper proposes an approach to recognize the printed Thai characters using the hybrid of global feature, local features, fuzzy membership function and the neural network. The global feature classifies all characters into seven main groups. Then the local features and the neural network are applied to identify the characters. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dynamic system identification using recurrent neural network with multi-valued connection weight(2009-01-01); Ruxpakawong, PhongthepThis paper introduces a new concept of the connection weight to the standard recurrent neural networks - Elman and Jordan networks. The architecture of the modified networks is the same as that of the original recurrent neural networks. However, in the modified networks the weight of each connection is multi-valued, depending on the value of the input data involved. The backpropagation learning algorithm is also modified to suit the proposed concept. The modified networks have been benchmarked against their original counterparts. The results on eleven benchmark problems are very encouraging. ©2009 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Feedforward neural network with multi-valued connection weights(2009-09-11); Ruxpakawong, PhongthepThis paper introduces a new concept of the connection weight to the multi-layer feedforward neural network. The architecture of the proposed approach is the same as that of the original multi-layer feedforward neural network. However, the weight of each connection is multi-valued, depending on the value of the input data involved. The backpropagation learning algorithm was also modified to suit the proposed concept. This proposed model has been benchmarked against the original feedforward neural network and the radial basis function network. The results on six benchmark problems are very encouraging. © 2009 Springer Berlin Heidelberg. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Nonlinear dynamic system identification using recurrent neural network with multi-segment piecewise-linear connection weight(2010-12-01); Ruxpakawong, PhongthepThis paper introduces a new concept of the connection weight to the standard recurrent neural networks-Elman and Jordan networks. The architecture of the modified networks is the same as that of the original recurrent neural networks. However, unlike the original recurrent neural networks whose connection weight is a single real number, in the modified networks the weight of each connection is multi-valued, depending on the value of the input data involved. The backpropagation learning algorithm is also modified to suit the proposed concept. The modified networks have been benchmarked against the feedforward neural network and the original recurrent neural networks. The experimental results on twelve benchmark problems show that the modified networks are clearly superior to the other three methods. © 2010 Springer-Verlag.