Bounding NBLM neighbourhood's adequate sizes

R. Mayoral, G. Lera
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Abstract

We try to address the problem of a priori selection of the adequate size for NBLM neighbourhoods. The application of the concept of neural neighbourhood to the Levenberg-Marquardt optimization method led us to the development of the NBLM algorithm. When this algorithm is used, there can be neighbourhoods that, not only produce significant reductions in memory requirements, but that also achieve better time performance than that of the Levenberg-Marquardt method. However, as long as the problem of choosing an appropriate neighbourhood size is not solved, the NBLM algorithm will not be able to offer the best possible performance.
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限定NBLM社区的足够大小
我们试图解决为NBLM社区选择适当大小的先验问题。将神经邻域概念应用到Levenberg-Marquardt优化方法中,导致了NBLM算法的发展。当使用该算法时,可能存在这样的邻域,不仅可以显著减少内存需求,而且还可以获得比Levenberg-Marquardt方法更好的时间性能。然而,只要选择合适的邻域大小的问题没有解决,NBLM算法就不能提供最好的性能。
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