Design of synthesizing multi-valued high-capacity auto-associative memories based on complex-valued networks
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所属单位：理学院
发表刊物：Lect. Notes Comput. Sci.
摘要：This paper presents a novel design method which is aimed to synthesize arbitrary multi-valued auto-associative memories via complex-valued neural networks. Globally exponential stable criteria are obtained to guarantee that the unique storage prototype can be retrieved. The proposed procedure enables auto-associative memories to be synthesized by satisfying the constraints of inequalities rather than the learning procedure. The main emphasis of the research presented here is on multi-valued high-capacity auto-associative memories via complex-valued networks. The designed auto-associative memories with (2r+2)n high memory capacities are robust with respect to design parameter selection and extend the scope of application of complex-valued neural networks. The approach of external inputs via complex-valued neural networks avoids spurious equilibria and retrieves the stored patters accurately. Some applicable experiments are given to illustrate the effectiveness and superiority. © 2018, Springer Nature Switzerland AG.
ISSN号：0302-9743
是否译文：否
发表时间：2018-01-01
合写作者：赵洪涌
通讯作者：沙春林