Abstract
This paper presents the growing greedy search algorithm and its application to associative memories of hysteresis neural networks in which storage of desired memories are guaranteed. In the algorithm, individuals correspond to cross-connection parameters, the cost function evaluates the number of spurious memories, and the set of individuals can grow depending on the global best. Performing basic numerical experiments, the algorithm efficiency is investigated.
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CITATION STYLE
Yamaoka, K., & Saito, T. (2015). Growing greedy search and its application to hysteresis neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9491, pp. 315–322). Springer Verlag. https://doi.org/10.1007/978-3-319-26555-1_36
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