Abstract
The competitive layer model (CLM) can be described by the optimization problem that is formulated with the CLM energy function. The minimum points of CLM energy function can be achieved by running some proper recurrent neural networks. In other words, the CLM can be implemented by the recurrent neural networks. This paper proposes the discrete-time linear threshold recurrent networks to solve the CLM problem. The conditions for the stable attractors of the networks are obtained, which just correspond to the conditions of the minimum points of CLM energy function established in the literature before. Therefore, the proposed network can be used to implement the CLM.
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Zhang, L., Heng, P.A., Yi, Z. (2009). Solving the CLM Problem by Discrete-Time Linear Threshold Recurrent Neural Networks. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_102
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DOI: https://doi.org/10.1007/978-3-642-04274-4_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04273-7
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