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Neural Computing and Applications

Erschienen in:

04.03.2017 | Original Article

Functional link neural network approach to solve structural system identification problems

verfasst von: Deepti Moyi Sahoo, S. Chakraverty

Erschienen in: Neural Computing and Applications | Ausgabe 11/2018

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Abstract

System identification problems are generally inverse vibration problems. Sometimes it is difficult to handle the inverse problems by traditional methods and classical artificial neural network. As such, the objective of this paper is to identify structural parameters by developing a novel functional link neural network (FLNN) model. FLNN model is more efficient than multi-layer neural network (MNN) as computation is less because hidden layer is not required. Here, single-layer neural network with multi-input and multi-output with feed-forward neural network model and principle of error back propagation has been used to identify structural parameters. The hidden layer is excluded by enlarging the input patterns with the help of Legendre and Hermite polynomials. Comparison of results among MNN, Legendre neural network, Hermite neural network and desired is considered and it is found that FLNN models are more effective than MNN.

Vorheriger Artikel Tracking topology structure adaptively with deep neural networks

Nächster Artikel Almost periodic dynamics of the delayed complex-valued recurrent neural networks with discontinuous activation functions

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Titel: Functional link neural network approach to solve structural system identification problems
verfasst von: Deepti Moyi Sahoo
S. Chakraverty
Publikationsdatum: 04.03.2017
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 11/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-017-2907-x

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