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Neural Computing and Applications

Erschienen in:

01.03.2010 | Original Article

Learning as a nonlinear line of attraction in a recurrent neural network

verfasst von: Ming-Jung Seow, Vijayan K. Asari, Adam Livingston

Erschienen in: Neural Computing and Applications | Ausgabe 2/2010

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Abstract

A method to embed N dimensional, multi-valued patterns into an auto-associative memory represented as a nonlinear line of attraction in a fully connected recurrent neural network is presented in this paper. The curvature of the nonlinear attractor is defined by the Kth degree polynomial line which best fits the training data in N dimensional state space. The width of the nonlinear line is then characterized by the statistical characteristics of the training patterns. Stability of the recurrent network is verified by analyzing the trajectory of the points in the state space during convergence. The performance of the network is benchmarked through the reconstruction of original gray-scale images from their corrupted versions. It is observed that the proposed method can quickly and successfully reconstruct each image with an average convergence rate of 3.10 iterations.

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Titel: Learning as a nonlinear line of attraction in a recurrent neural network
verfasst von: Ming-Jung Seow
Vijayan K. Asari
Adam Livingston
Publikationsdatum: 01.03.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 2/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-009-0304-9

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