Skip to main content
SpringerLink
Log in

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

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10):993–1001. https://doi.org/10.1109/34.58871

    Article  Google Scholar 

  2. Zhou ZH, Chen SF (2002) Neural network ensemble. Chin J Comput 25(1):1–8. https://doi.org/10.3321/j.issn:0254-4164.2002.01.001 (in Chinese with abstract in English)

    Article  MathSciNet  Google Scholar 

  3. Zhou ZH, Wu JX, Wei T (2002) Ensembling neural networks: many could be better than all. Artif Intell 137:239–267. https://doi.org/10.1016/S0004-3702(02)00190-X

    Article  MathSciNet  MATH  Google Scholar 

  4. Yu HL, Liu DY, Chen GF (2010) A neural network ensemble method for precision fertilization modeling. Math Comput Model 51:1375–1382. https://doi.org/10.1016/j.mcm.2009.10.028

    Article  Google Scholar 

  5. Bologna G (2004) Is it worth generating rules from neural network ensembles? J Appl Log 2:325–348. https://doi.org/10.1016/j.jal.2004.03.004

    Article  MathSciNet  MATH  Google Scholar 

  6. Oyewole SA, Olugbara OO (2018) Product image classification using Eigen color feature with ensemble machine learning. Egypt Inform J 19:83–100. https://doi.org/10.1016/j.eij.2017.10.002

    Article  Google Scholar 

  7. Cárdenas-Gallo I, Sarmiento CA, Morales GA et al (2017) An ensemble classifier to predict track geometry degradation. Reliab Eng Syst Saf 161:53–60. https://doi.org/10.1016/j.ress.2016.12.012

    Article  Google Scholar 

  8. Li H, Wang XS, Ding SF (2016) Research of multi-sided multi-granular neural network ensemble optimization method. Neurocomputing 197:78–85. https://doi.org/10.1016/j.neucom.2016.02.013

    Article  Google Scholar 

  9. González M, Dominguez D, Sánchez Á et al (2017) Increase attractor capacity using an ensembled neural network. Expert Syst Appl 71:206–215. https://doi.org/10.1016/j.eswa.2016.11.035

    Article  Google Scholar 

  10. Zhao ZS, Feng X, Lin YY et al (2015) Evolved neural network ensemble by multiple heterogeneous swarm intelligence. Neurocomputing 49:29–38. https://doi.org/10.1016/j.neucom.2013.12.062

    Article  Google Scholar 

  11. Yin XC, Huang K, Yang C, Hao HW (2014) Convex ensemble learning with sparsity and diversity. Inf Fusion 20(1):49–59. https://doi.org/10.1016/j.inffus.2013.11.003

    Article  Google Scholar 

  12. Yang J, Zeng X, Zhong S, Wu S (2013) Effective neural network ensemble approach for improving generalization performance. IEEE Trans Neural Net Learn Syst 24(6):878–887. https://doi.org/10.1109/TNNLS.2013.2246578

    Article  Google Scholar 

  13. Li H, Wang X, Ding S (2018) Research and development of neural network ensembles: a survey. Artif Intell Rev 49:455–479. https://doi.org/10.1007/s10462-016-9535-1

    Article  Google Scholar 

  14. Mohamad M, Makhtar M, Rahman MNA (2017) The reconstructed heterogeneity to enhance ensemble neural network for large data. In: Herawan T, Ghazali R, Nawi N, Deris M (eds) Recent advances on soft computing and data mining. SCDM 2016. Advances in intelligent systems and computing, vol 549. Springer, Cham, pp 447–455. https://doi.org/10.1007/978-3-319-51281-5_45

    Chapter  Google Scholar 

  15. Nanni L, Ghidoni S, Brahnam S (2018) Ensemble of convolutional neural networks for bioimage classification. Appl Comput Inform. https://doi.org/10.1016/j.aci.2018.06.002 ((In Press))

    Article  Google Scholar 

  16. Chen YT, Chang HB, Meng J, Zhang DX (2019) Ensemble Neural Networks (ENN): a stochastic method. Neural Netw 110:170–185. https://doi.org/10.1016/j.neunet.2018.11.009

    Article  Google Scholar 

  17. Kumar I, Bhadauria HS, Jitendra V, Shruti T (2017) A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern Biomed Eng 37:217–228. https://doi.org/10.1016/j.bbe.2017.01.001

    Article  Google Scholar 

  18. Wang R, Li W, Zhang L (2019) Blur image identification with ensemble convolution neural networks. Signal Process 155:73–82. https://doi.org/10.1016/j.sigpro.2018.09.027

    Article  Google Scholar 

  19. Asakura T, Date Y, Kikuchi J (2018) Application of ensemble deep neural network to metabolomics studies. Anal Chim Acta 1037:230–236. https://doi.org/10.1016/j.aca.2018.02.045

    Article  Google Scholar 

  20. Zhang L, Yu GX, Xia DW, Wang J (2019) Protein–protein interactions prediction based on ensemble deep neural networks. Neurocomputing 324:10–19. https://doi.org/10.1016/j.neucom.2018.02.097

    Article  Google Scholar 

  21. Kaveh K, Bui MD, Rutschmann P (2019) Integration of artificial neural networks into TELEMAC-MASCARET system, new concepts for hydromorphodynamic modeling. Adv Eng Softw 132:18–28. https://doi.org/10.1016/j.advengsoft.2019.03.011

    Article  Google Scholar 

  22. Geng ZQ, Shang DR, Han YM, Zhong YH (2019) Early warning modeling and analysis based on a deep radial basis function neural network integrating an analytic hierarchy process: a case study for food safety. Food Control 96:329–342. https://doi.org/10.1016/j.foodcont.2018.09.027

    Article  Google Scholar 

  23. Ge L, Ge LJ, Hu J (2019) Feature extraction and classification of hand movements surface electromyogram signals based on multi-method integration. Neural Process Lett 49(3):1179–1188. https://doi.org/10.1007/s11063-018-9862-0

    Article  Google Scholar 

  24. Li J, Yu ZL, Gu Z et al (2019) MMAN: multi-modality aggregation network for brain segmentation from MR images. Neurocomputing 358:10–19. https://doi.org/10.1016/j.neucom.2019.05.025?

    Article  Google Scholar 

  25. Verikas A, Lipnickas A (2002) Fusing neural networks through space partitioning and fuzzy integration. Neural Process Lett 16(1):53–65. https://doi.org/10.1023/A:1019703911322

    Article  MATH  Google Scholar 

  26. Davidson JL, Ritter GX (1990) Theory of morphological neural networks. In: Proceedings of the SPIE. Digital Optical Computing II. Los Angeles, CA, USA, vol 1215, pp 378-388.https://doi.org/10.1117/12.18085

  27. Feng NQ, Sun B (2018) On simulating one-trial learning using morphological neural networks. Cogn Syst Res 53:61–70. https://doi.org/10.1016/j.cogsys.2018.05.003

    Article  Google Scholar 

  28. Ritter GX, Sussner P, Diaz-de-Leon JL (1998) Morphological associative memories. IEEE Trans Neural Netw 9(2):281–293. https://doi.org/10.1109/72.661123

    Article  Google Scholar 

  29. Wang M, Wang ST, Wu XJ (2003) Initial results of fuzzy morphological associative memories. Acta Electronica Cinica 31(5):690–693 (in Chinese with abstract in English)

    MathSciNet  Google Scholar 

  30. Wang M, Chen SC (2005) Enhanced FMAM based on empirical kernel map. IEEE Trans Neural Netw 16(3):557–564

    Article  Google Scholar 

  31. Feng NQ, Liu CH, Zhang CP et al (2010) Research on the framework of morphological associative memories. Chin J Comput 33(1):157–166. https://doi.org/10.3724/SP.J.1016.2010.00157 (in Chinese with abstract in English)

    Article  Google Scholar 

  32. Feng NQ, Tian Y, Wang XF et al (2015) Logarithmic and exponential morphological associative memories. Ruan Jian Xue Bao/J Softw 26(7):1662–1674 (in Chinese with abstract in English)

    MathSciNet  Google Scholar 

  33. Feng NQ, Wang XF, Mao WT, Ao LH (2013) Heteroassociative morphological memories based on four-dimensional storage. Neurocomputing 116:76–86. https://doi.org/10.1016/j.neucom.2012.01.043

    Article  Google Scholar 

  34. Feng NQ, Yao YL (2016) No rounding reverse fuzzy morphological associative memories. Neural Netw World 6:571–587

    Article  Google Scholar 

  35. Valdiviezo-N JC, Urcid G, Lechuga E (2016) Digital restoration of damaged color documents based on hyperspectral imaging and lattice associative memories. Signal Image Video Process. https://doi.org/10.1007/s11760-016-1042-y

    Article  Google Scholar 

  36. Graña M, Chyzhyk D (2015) Image understanding applications of lattice autoassociative memories. IEEE Trans Neural Netw Learn Syst 27(9):1920–1932. https://doi.org/10.1109/TNNLS.2015.2461451

    Article  MathSciNet  Google Scholar 

  37. Sussner P, Schuster T (2018) Interval-valued fuzzy morphological associative memories: some theoretical aspects and applications. Inf Sci 438:127–144. https://doi.org/10.1016/j.ins.2018.01.042

    Article  MathSciNet  MATH  Google Scholar 

  38. Acevedo ME, Martınez JA, Acevedo MA et al (2014) Morphological associative memories for gray-scale image encryption. Appl. Math. Inf. Sci. 8(1):127–134

    Article  MathSciNet  Google Scholar 

  39. Sossa H, Guevara E (2014) Efficient training for dendrite morphological neural networks. Neurocomputing 131:132–142. https://doi.org/10.1016/j.neucom.2013.10.031

    Article  Google Scholar 

  40. He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284

    Article  Google Scholar 

  41. Li J, Yu ZL, Gu Z et al (2019) Spatial-temporal discriminative restricted boltzmann machine for event-related potential detection and analysis. IEEE Trans Neural Syst Rehabil Eng 27(2):139–151

    Article  Google Scholar 

  42. Wu W, Chen Z, Gao X et al (2014) Probabilistic common spatial patterns for multichannel EEG analysis. IEEE Trans Pattern Anal Mach Intell 37(3):639–653. https://doi.org/10.1109/TPAMI.2014.2330598

    Article  Google Scholar 

  43. Li J, Yu ZL, Gu Z et al (2018) A hybrid network for ERP detection and analysis based on restricted Boltzmann machine. IEEE Trans Neural Syst Rehabil Eng 26(3):563–572. https://doi.org/10.1109/TNSRE.2018.2803066

    Article  Google Scholar 

  44. Qi F, Li Y, Wu W (2015) RSTFC: a novel algorithm for spatio-temporal filtering and classification of single-trial EEG. IEEE Trans Neural Netw Learn Syst 26(12):3070–3082. https://doi.org/10.1109/TNNLS.2015.2402694

    Article  MathSciNet  Google Scholar 

  45. Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159

    Article  Google Scholar 

  46. Liu Y, Qin Z, Lu J, Shi Z (2005) Multimodal particle swarm optimization for neural network ensemble. J Comput Res Dev 42(9):1519–1526 (in Chinese with abstract in English)

    Article  Google Scholar 

  47. Li K, Huang HK (2006) Study of a neural network ensemble algorithm for small data sets. J Comput Res Dev 43(7):1161–1166

    Article  Google Scholar 

  48. Gupta Y (2018) Selection of important features and predicting wine quality using machine learning techniques. Procedia Comput Sci 125:305–312. https://doi.org/10.1016/j.procs.2017.12.041

    Article  Google Scholar 

  49. Vazquez RA, Sossa H (2011) Behavioral study of median associative memory under true color image patterns. Neurocomputing 74(17):2985–2997

    Article  Google Scholar 

Download references

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.

Author information

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

Authors
  1. Naiqin Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiuqin Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Naiqin Feng, Xiuqin Geng or Bin Sun.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-021-10569-9

Keywords

Search

Navigation