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A classifier of matrix modular neural network to simplify complex classification tasks

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This paper proposes the matrix modular neural network (MMNN), which is a modular neural network and adopts a novel task decomposition technique to solve complex problems, such as the large training sets and the category asymmetric training sets. A complex problem can be decomposed into many easier problems, each of which is dealt in two subspaces and can be solved by a single neural network module. All of these modules form a neural network matrix, which produces an output matrix that leads to an integration machine so that finally a classification decision result can be efficiently made. This paper’s theoretic analyses and experiments show that the MMNN can reduce the learning time and improve the generalization capability and the classification accuracy of neural networks.

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  1. 1.

    Wang LC, Der SZ, Nasrabadi NM (1998) Automatic target recognition using a feature decomposition and data-decomposition modular neural network. IEEE Trans Image Process 7(8):1113–1122

  2. 2.

    Hou Y, Du J, Wang M (2007) Neural network. Xidian University Press, Xi’an, pp 125–140

  3. 3.

    Xie W, Shi Y, Xiao P (2010) Classification of natural image based on BP neural network. Comput Eng Appl 46(2):163–166

  4. 4.

    Zhao Z, Huang D (2004) Human face recognition based on multi-features using neural networks committee. Pattern Recognit Lett 25(12):1351–1358

  5. 5.

    Oshaba AS, Ali ES, Elazim SMA (2017) PI controller design for MPPT of photovoltaic system supplying SRM via BAT search algorithm. Neural Comput Appl 4(28):651–667

  6. 6.

    Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman and Hall, London

  7. 7.

    Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

  8. 8.

    Shen X, Zhou Z (2000) A survey of boosting and bagging. Comput Eng Appl 36(12):31–32

  9. 9.

    Nilsson NJ (1965) Learning machines: foundations of trainable pattern-classifying systems. McGraw-Hill, New York

  10. 10.

    Ali ES, Elazim SMA, Abdelaziz AY (2016) Ant lion optimization algorithm for renewable distributed generations. Energy 11(116):445–458

  11. 11.

    Anand R, Mehrotra K, Mohan CK et al (1995) Efficient classification for multiclass problems using modular neural networks. IEEE Trans Neural Netw 6(1):117–124

  12. 12.

    Saengrung A, Abtahi A, Zilouchian A (2007) Neural network model for a commercial PEM fuel cell system. J Power Sources 172(2):749–759

  13. 13.

    Si C (2010) Learning rate parameter improve methods for BP neutral network. J Chang Normal Univ Nat Sci 29(1):26–28

  14. 14.

    Hong-mei W (2004) A neural networks multi-classifers integrated scheme based on OCON for handwritten Chinese character recognition. Comput Eng 30(16):151–152

  15. 15.

    Shuai F (2008) Research and application of the co-operative modular neural network. Wuhan University of Technology, Wuhan

  16. 16.

    Sun JX (2002) Modern pattern recognition. National University of Defense Technology Publishers, Changsha

  17. 17.

    Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20:226–239

  18. 18.

    Hester T, Stone P (2013) TEXPLORE: real-time sample-efficient reinforcement learning for robots. Mach Learn 90(3):385–429

  19. 19.

    Zhang Y, Liu C (2003) A novel face recognition method based on linear discriminant analysis. J Infrared Millim Waves 22(5):327–330

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Correspondence to Ping Hu.

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Hu, P. A classifier of matrix modular neural network to simplify complex classification tasks. Neural Comput & Applic 32, 1367–1377 (2020).

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  • Modular neural networks
  • Task decomposition
  • Computational complexity
  • Generalization capability