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Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2004 / Cilt: 4 - Sayı: 1

Missed data forecasting using fast neural network architecture

Abdullah BAL
Pages
1051–1056
Publication Date
2022-10-18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

WNN performance is tested for function approximation and missed data forecasting problems.

📌 3 Key Takeaways

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Objective & Hypothesis:Wavelet function based feed forward neural network architecture is proposed for forecasting of missed data.
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Key Findings & Data:A set of wavelet functions offers a multi-resolution approximation in signal analysis and provides localization in spatial domain.
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Conclusion & Impact:WNN performance is tested for function approximation and missed data forecasting problems.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Wavelet function based feed forward neural network architecture is proposed for forecasting of missed data. A set of wavelet functions offers a multi-resolution approximation in signal analysis and provides localization in spatial domain. Wavelet neural network (WNN) is developed using these properties of wavelet functions in the traditional feed forward network. This network has multi-layer architecture and its each neuron includes wavelet functions in the first layer. The second layer entries are wavelet coefficients corresponding to first layer outputs. Wavelet functions provide high convergence capability and faster learning process in the multilayer networks. WNN performance is tested for function approximation and missed data forecasting problems.

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Cite This Article

BAL, A. (2022). Missed data forecasting using fast neural network architecture. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 4 - Sayı: 1, 1051–1056. https://ajindex.com/articles/543893

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References (2)

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  2. Lefkosa, Cyprus. [3] Zhang, Q. and Benveniste A., (1992), “Wavelet Networks“, IEEE Trans. On Neural Networks, 3(6): 889-898, Nov. 1992, [4] Albora M, Bal A. and Ucan O.N.,(2002), “Wavelet Based Design of Dynamical Neural Networks for Magnetic Map Processing”, International Conference on Earth Science and Electronics, ICESE’2002, Istanbul-Turkey, 24-25 October 2002. [5] S. Pittner, S. V. Kamarthi and Q. Gao, “Wavelet Networks for sensor signal classification in flank wear assessment”, Journal of Intelligent Manufacturing, 9,315-322, 1998. [6] Bal A. (2002) “Wavelet Neural Network and Learning Algorithm”, PhD Dissertation, YTU, 2002 [7] Rao, R.M. and Bopardikar, A.S., Wavelet Transforms Introduction to Theory And Applications, Addison Wesley Longman,USA [8] Vetterli Martin, Kovacevic Jelena, Wavelets and Sub-band Coding, Prentice Hall, New Jersey, USA. [9] Hagan, T.M.,Demuth, H. B. and Beale, M. Neural Networks Design, PWS Publishing Company, Boston, USA.