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A probability density function generator based on neural networks

Author

Listed:
  • Chen, Chi-Hua
  • Song, Fangying
  • Hwang, Feng-Jang
  • Wu, Ling

Abstract

In order to generate a probability density function (PDF) for fitting the probability distributions of practical data, this study proposes a deep learning method which consists of two stages: (1) a training stage for estimating the cumulative distribution function (CDF) and (2) a performing stage for predicting the corresponding PDF. The CDFs of common probability distributions can be utilised as activation functions in the hidden layers of the proposed deep learning model for learning actual cumulative probabilities, and the differential equation of the trained deep learning model can be used to estimate the PDF. Numerical experiments with single and mixed distributions are conducted to evaluate the performance of the proposed method. The experimental results show that the values of both CDF and PDF can be precisely estimated by the proposed method.

Suggested Citation

  • Chen, Chi-Hua & Song, Fangying & Hwang, Feng-Jang & Wu, Ling, 2020. "A probability density function generator based on neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318710
    DOI: 10.1016/j.physa.2019.123344
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    References listed on IDEAS

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    1. SangSik Lee & YiNa Jeong & SuRak Son & ByungKwan Lee, 2019. "A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning," Sustainability, MDPI, vol. 11(13), pages 1-21, July.
    2. Chang, Ching-Ter & Lee, Hsing-Chen, 2016. "Taiwan's renewable energy strategy and energy-intensive industrial policy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 456-465.
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    Cited by:

    1. Chi-Hua Chen & Kuo-Ming Chao & Feng-Jang Hwang & Chunjia Han & Lianrong Pu, 2021. "Editorial," International Journal of Distributed Sensor Networks, , vol. 17(2), pages 15501477219, February.

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