NDLI: Extension of a classical error functional and structure modification of continuous Hopfield neural networks

Content Provider	IEEE Xplore Digital Library
Author	Kwiatkowska-Murzyn, A.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Enigneering Comput. Sci. & Telecommun., Univ. of Zielona Gora, Gora (Kwiatkowska-Murzyn, A.)
Abstract	This paper addresses the problem of training multiple trajectories by means of continuous Hopfield neural networks in identification a control model of the financial flows of the Polish economy. There are two drawbacks of the networks learning procedure when solving this problem. First, a strong network sensitivity to small changes in the network weights as a result of multiple, nonlinear connections between its variables. Second, a poor quality of the network mapping resulting from the finiteness of the learning set describing unique properties of that system. To overcome these constraints, a few modifications of the basic learning procedure have been proposed. The crucial idea here considers extension of a classical error functional to three forms of penalty term, depending on the number of available data and the structure modification.
Starting Page	428
Ending Page	433
File Size	563613
Page Count	6
File Format	PDF
ISBN	9781424415427
DOI	10.1109/HSI.2008.4581477
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-25
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Sensitivity network sensitivity Biological system modeling Neurons Artificial neural networks penalty term mapping curvature Performance analysis Mathematical model regularization parameter
Content Type	Text
Resource Type	Article

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