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Structural Parameter Identification Using Interval Functional Link Neural Network

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Recent Trends in Wave Mechanics and Vibrations

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

This paper presents a procedure to identify uncertain structural parameters of multistorey shear buildings by interval functional link neural network. The structural parameters are identified using the response of the structure with both ambient and forced vibration. Here interval functional link neural network has been used to train interval data. The polynomials used in the functional link are Chebyshev polynomial. Different degrees of Chebyshev polynomial is used for training and further it is tested with interval Legendre polynomial using the stored converged weights of ChNN. These polynomials are taken in interval form. It is seen that by using interval functional link neural network the computational time is very less compared to interval neural network. Accordingly example problems of two and five-storey shear buildings have been analyzed for free and forced vibration case to show the efficiency of the IFLNN model.

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Author information

Authors and Affiliations

  1. Department of Mathematics, School of Science, O.P. Jindal University, Punjipathra, Raigarh, 496001, Chhattisgarh, India

    Deepti Moyi Sahoo

  2. Department of Mathematics, National Institute of Technology Rourkela, Rourkela, 769008, Odisha, India

    S. Chakraverty

Authors
  1. Deepti Moyi Sahoo
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  2. S. Chakraverty
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Corresponding author

Correspondence to Deepti Moyi Sahoo .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Rourkela, Rourkela, Odisha, India

    S. Chakraverty

  2. Vibration Research Group, Von karman society, Jalpaiguri, India

    Paritosh Biswas

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Sahoo, D.M., Chakraverty, S. (2020). Structural Parameter Identification Using Interval Functional Link Neural Network. In: Chakraverty, S., Biswas, P. (eds) Recent Trends in Wave Mechanics and Vibrations. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-0287-3_12

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  • DOI: https://doi.org/10.1007/978-981-15-0287-3_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0286-6

  • Online ISBN: 978-981-15-0287-3

  • eBook Packages: EngineeringEngineering (R0)

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