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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28624

Title: Scale Equalized Higher-order Neural Networks
Authors: Chien-Ming Lin;Keng-Hsuan Wu;Jung-Hua Wang
Contributors: NTOU:Department of Electrical Engineering
Keywords: SEHNN;Scale Equalization;Higher-order Neural Network;function approximation
Date: 2005-10
Issue Date: 2011-10-21T02:38:35Z
Publisher: 2005 IEEE International Conference on Systems, Man and Cybernetics
Abstract: Abstract:This paper presents a novel network, called Scale Equalized Higher-order Neural Network (SEHNN) based on concept of Scale Equalization (SE). We show that SE is particularly useful in alleviating the scale divergence problem that plagues higher-order networks. SE comprises two main processes: setting the initial weight vector and conducting the matrix transformation. An illustrative embodiment of SEHNN is built on the Sigma-Pi Network (SPN) applied to task of function approximation. Empirical results verify that SEHNN outperforms other higher-order networks in terms of computation efficiency. Compared to SPN, and Pi-Sigma Network (PSN), SEHNN requires less number of epochs to complete the training process.
Relation: 1, pp.816-821
URI: http://ntour.ntou.edu.tw/handle/987654321/28624
Appears in Collections:[電機工程學系] 演講及研討會

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