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JACIII Vol.17 No.3 pp. 443-449
doi: 10.20965/jaciii.2013.p0443
(2013)

Paper:

A Neural Network Structure Decomposition Based on Pruning and its Visualization Method

Atsushi Shibata, Jiajun Lu, Fangyan Dong,
and Kaoru Hirota

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Received:
December 2, 2012
Accepted:
March 25, 2013
Published:
May 20, 2013
Keywords:
neural network, pruning, visualization, UCI dataset, 6 leg robot
Abstract

To decompose neural network structures for composite tasks, a pruning method and its visualization method are proposed. Visualization by placing the neurons on a 2D plane clarifies the structure related to each composited task. Experiments on a composite task using two tasks from a UCI dataset show that the neural network of the composite task contains more than 80% of neurons. The proposed methods target the transfer learning of robot motion, and results of an adaptation experiments are also referred.

Cite this article as:
Atsushi Shibata, Jiajun Lu, Fangyan Dong, and
and Kaoru Hirota, “A Neural Network Structure Decomposition Based on Pruning and its Visualization Method,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.3, pp. 443-449, 2013.
Data files:
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Last updated on Mar. 01, 2021