Synthesization of Multi-valued Associative High-Capacity Memory Based on Continuous Networks with a Class of Non-smooth Linear Nondecreasing Activation Functions
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所属单位：理学院
发表刊物：Neural Process Letters
摘要：This paper presents a novel design method for multi-valued auto-associative and hetero-associative memories based on a continuous neural network (CNN) with a class of non-smooth linear nondecreasing activation functions. The proposed CNN is robust in terms of the design parameter selection, which is dependent on a set of inequalities rather than the learning procedure. Some globally exponentially stable criteria are obtained to ensure multi-valued associative patterns to be retrieved accurately. The methodology, by generating CNN where the input data are fed via external inputs, avoids spurious memory patterns and achieves (2 r) n storage capacity. These analytic results are applied to the associative memory of images. The fault-tolerant capability and the effectiveness are validated by illustrative experiments. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
ISSN号：1370-4621
是否译文：否
发表时间：2019-08-15
合写作者：赵洪涌,Yuan, Yuan,Bai, Yuzhen
通讯作者：沙春林