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Study on Neural Network Integration Method Based on Morphological Associative Memory Framework

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Abstract

In traditional neural network integration, people adopt Boosting, Bagging and other methods to integrate traditional neural networks. The integration is complex, time-consuming and laborious, difficult to popularize and apply. This paper is not a continuation of this method, but another integration which is called by us morphological neural network integration (MNNI) or morphological associative memory integration (MAMI). These networks used in MAMI are a network family, with 10 family members, unified in the morphological associative memory framework. Various morphological associative memory networks can be directly used as individual networks to learn and work separately, and then synthesize to draw conclusions. The results of some experiments show that this method is not only feasible in theory, but also effective in practice. It can avoid the complexity of traditional integration method, make the integration structure simple and clear, easy to operate, save time, and therefore is a method of neural network integration with research and application value. The contribution of this paper lies in that: (1) it proposed the concept and method of MNNI and, (2) verified the effectiveness of MNNI through experiments and, (3) it has the characteristics of simplicity, saving time and labor and cost, with a good application prospect and, (4) thus promoting the development of morphological neural networks in theory and practice.

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Acknowledgements

This work was supported by Henan Province’s key R&D Project under Grant 192102310217, the Science and Technology Research Project of Zhengzhou City under Grant 153PKJGG153, the Key Research Project of Zhengzhou University of Industrial Technology under Grant JG-190101 and the Industry-Education Joint Fund of the Science and Technology Development Center of the Ministry of Education under Grant 2017A03016.

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Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University of Industrial Technology, Zhengzhou, 451150, China

    Naiqin Feng & Bin Sun

  2. Xinxiang Central Hospital, Xinxiang, 453000, China

    Xiuqin Geng

  3. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, China

    Naiqin Feng

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  1. Naiqin Feng
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  2. Xiuqin Geng
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Correspondence to Naiqin Feng, Xiuqin Geng or Bin Sun.

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Feng, N., Geng, X. & Sun, B. Study on Neural Network Integration Method Based on Morphological Associative Memory Framework. Neural Process Lett 53, 3915–3945 (2021). https://doi.org/10.1007/s11063-021-10569-9

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