Abstract
This paper proposes an innovative enhancement of the classical Hopfield network algorithm (and potentially its stochastic derivatives) with an “adaptation mechanism” to guide the neural search process towards high-quality solutions for large-scale static optimization problems. Specifically, a novel methodology that employs gradient-descent in the error space to adapt weights and constraint weight parameters in order to guide the network dynamics towards solutions is formulated. In doing so, a creative algebraic approach to define error values for each neuron without knowing the desired output values for the same is adapted.
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References
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OSC — Ohio Supercomputer Center, Columbus, Ohio, USA, 2002.
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© 2003 Springer-Verlag Berlin Heidelberg
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Serpen, G. (2003). Adaptive Hopfield Network. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_1
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DOI: https://doi.org/10.1007/3-540-44989-2_1
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