Abstract
One of artificial neural network models with non-equilibrium dynamics is S-GCM. This model in comparison to Hopfield method has more capacity storage and more success rate, but yet, as an associative memory system has some weakness such as small storage rate and low speed of convergence. In this paper, a new learning method for S-GCM is proposed. In the proposed method, we use modified sparse matrix for learning method. Both the theory analyses and computer simulation results show that the performance of S-GCM can be improved greatly by using our learning method.
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Rahimov, H., Jahedmotlagh, MR., Mozayani, N. (2006). A New Learning Method for S-GCM. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_155
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DOI: https://doi.org/10.1007/11941439_155
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
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