Citation

Singh, Y.P. and RoyChowdhury, Pinaki (2001) Dynamic tunneling based regularization in feedforward neural networks. Artificial Intelligence, 131 (1-2). pp. 55-71. ISSN 00043702

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Abstract

This paper presents a new regularization method based on dynamic tunneling for enhancing generalization capability of multilayered neural networks. The proposed method enables escape through undesired sub-optimal solutions on the composite error surface by means of dynamic tunneling. Undesired sub-optimal solutions may be increased or introduced from regularized objective function. Hence, the proposed method is capable of enhancing the regularization property without getting stuck at sub-optimal values in search space. The regularization property and escape from the sub-optimal values have been demonstrated through computer simulations on two examples. (C) 2001 Elsevier Science B.V. All rights reserved.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	09 Sep 2011 03:15
Last Modified:	06 Feb 2014 04:22
URII:	http://shdl.mmu.edu.my/id/eprint/2685

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