NDLI: An algorithm for automatic design of two hidden layered artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Islaml, M.M. Shahjahan, M. Murase, K.
Copyright Year	2000
Description	Author affiliation: Dept. of Human & Artificial Intelligence Syst., Fukui Univ., Japan (Islaml, M.M.)
Abstract	This paper presents an algorithm that automatically designs compact two hidden layered artificial neural networks (ANNs). The algorithm, which we call the cascade neural network design algorithm (CNNDA), starts network building process in a constructive fashion by adding one node at a time. Once the network converges CNNDA starts pruning of the networks by deleting hidden nodes and connections sequentially. The pruning process is repeated until the network no longer converges. In order to improve the computational efficiency and convergence, CNNDA adopts a training process which temporarily freezes the input weights of a hidden node when the output of that node does not change much in the successive few iterations. However, the freezed weights may unfreeze in the pruning process for further modification. CNNDA has been tested on three benchmark, i.e., character recognition, cancer and diabetes, problems. The experimental results show that CNNDA can produce very compact ANNs with a small classification error in comparison with other algorithms. It is seen that there is a tradeoff between compact ANN size and training epochs.
Starting Page	467
Ending Page	472
File Size	533956
Page Count	6
File Format	PDF
ISBN	0769506194
ISSN	10987576
DOI	10.1109/IJCNN.2000.859439
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-07-27
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Artificial neural networks Convergence Computer architecture Buildings Computational efficiency Humans Artificial intelligence Design engineering Testing
Content Type	Text
Resource Type	Article

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