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Review on Methods to Fix Number of Hidden Neurons in Neural Networks

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  • K. Gnana Sheela
  • S. N. Deepa

Abstract

This paper reviews methods to fix a number of hidden neurons in neural networks for the past 20 years. And it also proposes a new method to fix the hidden neurons in Elman networks for wind speed prediction in renewable energy systems. The random selection of a number of hidden neurons might cause either overfitting or underfitting problems. This paper proposes the solution of these problems. To fix hidden neurons, 101 various criteria are tested based on the statistical errors. The results show that proposed model improves the accuracy and minimal error. The perfect design of the neural network based on the selection criteria is substantiated using convergence theorem. To verify the effectiveness of the model, simulations were conducted on real-time wind data. The experimental results show that with minimum errors the proposed approach can be used for wind speed prediction. The survey has been made for the fixation of hidden neurons in neural networks. The proposed model is simple, with minimal error, and efficient for fixation of hidden neurons in Elman networks.

Suggested Citation

  • K. Gnana Sheela & S. N. Deepa, 2013. "Review on Methods to Fix Number of Hidden Neurons in Neural Networks," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, June.
    • Handle: RePEc:hin:jnlmpe:425740
    DOI: 10.1155/2013/425740
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    Cited by:

    1. Zheng Zeng & Wei-Ge Luo & Zhe Wang & Fa-Cheng Yi, 2021. "Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(2), pages 1-17, January.
    2. Li, Hangxin & Wang, Shengwei, 2022. "Two-time-scale coordinated optimal control of building energy systems for demand response considering forecast uncertainties," Energy, Elsevier, vol. 253(C).
    3. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    4. Ayub, Yousaf & Hu, Yusha & Ren, Jingzheng, 2023. "Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models," Renewable Energy, Elsevier, vol. 215(C).
    5. Martín Pensado-Mariño & Lara Febrero-Garrido & Pablo Eguía-Oller & Enrique Granada-Álvarez, 2021. "Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    6. Maiorino, Angelo & Del Duca, Manuel Gesù & Aprea, Ciro, 2022. "ART.I.CO. (ARTificial Intelligence for COoling): An innovative method for optimizing the control of refrigeration systems based on Artificial Neural Networks," Applied Energy, Elsevier, vol. 306(PB).
    7. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    8. Muhammad Noman Shafique & Ammar Rashid & Sook Fern Yeo & Umar Adeel, 2023. "Transforming Supply Chains: Powering Circular Economy with Analytics, Integration and Flexibility Using Dual Theory and Deep Learning with PLS-SEM-ANN Analysis," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    9. Wang, Sen & Qin, Chaoxu & Feng, Qihong & Javadpour, Farzam & Rui, Zhenhua, 2021. "A framework for predicting the production performance of unconventional resources using deep learning," Applied Energy, Elsevier, vol. 295(C).
    10. Warut Pannakkong & Thanyaporn Harncharnchai & Jirachai Buddhakulsomsiri, 2022. "Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models," Energies, MDPI, vol. 15(9), pages 1-21, April.
    11. Osamah Basheer Shukur & Muhammad Hisyam Lee, 2015. "Imputation of Missing Values in Daily Wind Speed Data Using Hybrid AR-ANN Method," Modern Applied Science, Canadian Center of Science and Education, vol. 9(11), pages 1-1, October.
    12. Anh-Tu Nguyen & Shih-Hao Lu & Phuc Thanh Thien Nguyen, 2021. "Validating and Forecasting Carbon Emissions in the Framework of the Environmental Kuznets Curve: The Case of Vietnam," Energies, MDPI, vol. 14(11), pages 1-38, May.
    13. Jihoon Moon & Sungwoo Park & Seungmin Rho & Eenjun Hwang, 2019. "A comparative analysis of artificial neural network architectures for building energy consumption forecasting," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    14. Xu Huang & Jiaqi Zhang & Jessada Sresakoolchai & Sakdirat Kaewunruen, 2021. "Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete," Sustainability, MDPI, vol. 13(4), pages 1-26, February.
    15. Luis Alfonso Menéndez García & Fernando Sánchez Lasheras & Paulino José García Nieto & Laura Álvarez de Prado & Antonio Bernardo Sánchez, 2020. "Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms," Mathematics, MDPI, vol. 8(12), pages 1-22, December.
    16. Liébana-Cabanillas, Francisco & Marinković, Veljko & Kalinić, Zoran, 2017. "A SEM-neural network approach for predicting antecedents of m-commerce acceptance," International Journal of Information Management, Elsevier, vol. 37(2), pages 14-24.

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