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The Application in Handwritten Digit Recognition of Deep Belief Network Based on Improved Genetic Algorithm

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Advanced Information Networking and Applications (AINA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 227))

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Abstract

The initial connection weight and threshold value of RBM in DBN have a certain influence on the recognition effect of the network. In this paper, the MNIST database is used as the data sample, and the improved genetic algorithm is applied to optimize the initial weight and threshold value in RBM, and the programming is realized by MATLAB language. The simulation results show that the improved DBN model is better than the traditional DBN network model and the traditional genetic algorithm improved DBN model in handwritten numeral recognition.

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Notes

  1. 1.

    This work was supported by Langfang Science and Technology Research and Development Self Fin- ancing Project (2019011008); the Natural Science Foundation of Hebei  Province [no. F2018408040]; the Hebei Education Funds for Youth Project [no. QN2018047].

References

  1. Murru, N., Rossini, R.: A Bayesian approach for initialization of weights in backpropagation neural net with application to character recognition. Neuroco mputing 193, 192–205 (2016)

    Google Scholar 

  2. Hanx: Handwritten digital recognition based on GA-BP neural network. In: International Conference on Artificial Intelligence, Management Science and Electronic Commerce, pp. 5050–5052. IEEE (2011)

    Google Scholar 

  3. Zhang, X., Wu, L.: Handwritten digit recognition based on improved learning rate BP algorithm. In: 2nd International Conference on Information Engineering and Computer Scienc- e Proceedings, pp. 1–4. ICIECS (2010)

    Google Scholar 

  4. Song Xiaoru, W., Xue, G.S., et al.: Simulation study on handwritten numeral recognition based on deep neural network. Sci. Technol. Eng. 19(5), 193–196 (2019)

    Google Scholar 

  5. Savich, A.W., Moussa, M.: Resource efficient arithmetic effects on RBM neural network solution quality using MNIST. In: 2011 International Conference on Reconfigurable Computing and FPGAs (ReConFig), pp. 35–40 (2011)

    Google Scholar 

  6. Guan Baolin, B.A., Lideng, B.P.: Neural network handwritten numerical recognition based on improved genetic algorithm. Control Instr. Chem. Ind. 40, 778–784 (2012). in Chinese

    Google Scholar 

  7. Chuang, C., Chellali, R., Yin, X.: Speech emotion recognition based on improved genetic algorithm optimized BP neural network. Appl. Res. Comput. 2, 344–346 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Science, Langfang Normal College, Langfang, 065000, HeBei, China

    Zhu Hong-Xia & Lu Xiu-Hua

  2. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, 710048, ShanXi, China

    Zhu Hong-Ying

Authors
  1. Zhu Hong-Xia
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  2. Lu Xiu-Hua
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  3. Zhu Hong-Ying
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Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Hong-Xia, Z., Xiu-Hua, L., Hong-Ying, Z. (2021). The Application in Handwritten Digit Recognition of Deep Belief Network Based on Improved Genetic Algorithm. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-030-75078-7_2

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