IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v541y2020ics0378437119318710.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119318710
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.123344?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    2. Chuang, Ming-Tung & Chang, Shih-Yu & Hsiao, Ta-Chih & Lu, Yun-Ru & Yang, Tsung-Yeh, 2019. "Analyzing major renewable energy sources and power stability in Taiwan by 2030," Energy Policy, Elsevier, vol. 125(C), pages 293-306.
    3. You-How Go & Lin-Sea Lau & Kwang-Jing Yii & Wee-Yeap Lau, 2020. "Does energy efficiency affect economic growth? Evidence from aggregate and disaggregate levels," Energy & Environment, , vol. 31(6), pages 983-1006, September.
    4. Wang, Qiang & Kwan, Mei-Po & Fan, Jie & Zhou, Kan & Wang, Ya-Fei, 2019. "A study on the spatial distribution of the renewable energy industries in China and their driving factors," Renewable Energy, Elsevier, vol. 139(C), pages 161-175.
    5. Ge, Tao & Cai, Xuesen & Song, Xiaowei, 2022. "How does renewable energy technology innovation affect the upgrading of industrial structure? The moderating effect of green finance," Renewable Energy, Elsevier, vol. 197(C), pages 1106-1114.
    6. Mohsen Niazian & Gniewko Niedbała, 2020. "Machine Learning for Plant Breeding and Biotechnology," Agriculture, MDPI, vol. 10(10), pages 1-23, September.
    7. Sakah, Marriette & Diawuo, Felix Amankwah & Katzenbach, Rolf & Gyamfi, Samuel, 2017. "Towards a sustainable electrification in Ghana: A review of renewable energy deployment policies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 544-557.
    8. Yuneng Jiang & Yi Zhu & Yasi Tian, 2023. "Measuring the Urban Sprawl of a Mega-Urban Agglomeration Area Based on Multi-Dimensions with a Mechanical Equilibrium Model: A Case Study of the Yangtze River Delta, China," Land, MDPI, vol. 12(8), pages 1-22, August.
    9. Siraj Osman Omer, 2021. "Application of Bayesian Networks of Genotype by Environment Interaction Evaluation Under Plant Disease, Soil Types and Climate Condition-using Bayesia Lab," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 7(3), pages 158-166, 07-2021.
    10. Jamali, Mohammad-Bagher & Rasti-Barzoki, Morteza & Altmann, Jörn, 2023. "An evolutionary game-theoretic approach for investigating the long-term behavior of the industry sector for purchasing renewable and non-renewable energy: A case study of Iran," Energy, Elsevier, vol. 285(C).
    11. Shih-Ping Shen & Jung-Fa Tsai, 2022. "Evaluating the Sustainable Development of the Semiconductor Industry Using BWM and Fuzzy TOPSIS," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
    12. Gung, Roger R. & Huang, Chun-Che & Hung, Wen-I & Fang, Yu-Jie, 2020. "The use of hybrid analytics to establish effective strategies for household energy conservation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318710. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.