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Design of Synthesizing Multi-valued High-Capacity Auto-associative Memories Based on Complex-Valued Networks

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11301))

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Abstract

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.

H. Zhao—This work was supported by National Natural Science Foundation of China (Grant nos. 11571170 and 11501290).

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Acknowledgement

The authors would like to thank the anonymous referees and editors for their helpful suggestions, which have improved the quality of this paper. This work was supported by National Natural Science Foundation of China (Grant nos. 11571170 and 11501290).

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Authors and Affiliations

  1. Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Chunlin Sha & Hongyong Zhao

Authors
  1. Chunlin Sha
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  2. Hongyong Zhao
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Correspondence to Hongyong Zhao .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Sha, C., Zhao, H. (2018). Design of Synthesizing Multi-valued High-Capacity Auto-associative Memories Based on Complex-Valued Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_42

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  • DOI: https://doi.org/10.1007/978-3-030-04167-0_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04166-3

  • Online ISBN: 978-3-030-04167-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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