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Natural computing for mechanical systems research: a tutorial overview

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Natural computing for mechanical systems research: a tutorial overview. / Worden, Keith; Staszewski, Wieslaw J.; Hensman, James J.
In: Mechanical Systems and Signal Processing, Vol. 25, No. 1, 01.2011, p. 4-111.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Worden, K, Staszewski, WJ & Hensman, JJ 2011, 'Natural computing for mechanical systems research: a tutorial overview', Mechanical Systems and Signal Processing, vol. 25, no. 1, pp. 4-111. https://doi.org/10.1016/j.ymssp.2010.07.013

APA

Worden, K., Staszewski, W. J., & Hensman, J. J. (2011). Natural computing for mechanical systems research: a tutorial overview. Mechanical Systems and Signal Processing, 25(1), 4-111. https://doi.org/10.1016/j.ymssp.2010.07.013

Vancouver

Worden K, Staszewski WJ, Hensman JJ. Natural computing for mechanical systems research: a tutorial overview. Mechanical Systems and Signal Processing. 2011 Jan;25(1):4-111. Epub 2010 Nov 10. doi: 10.1016/j.ymssp.2010.07.013

Author

Worden, Keith ; Staszewski, Wieslaw J. ; Hensman, James J. / Natural computing for mechanical systems research : a tutorial overview. In: Mechanical Systems and Signal Processing. 2011 ; Vol. 25, No. 1. pp. 4-111.

Bibtex

@article{7b7b9a0956e84599b524c1321bfe0e5b,
title = "Natural computing for mechanical systems research: a tutorial overview",
abstract = "A great many computational algorithms developed over the past half-century have been motivated or suggested by biological systems or processes, the most well-known being the artificial neural networks. These algorithms are commonly grouped together under the terms soft or natural computing. A property shared by most natural computing algorithms is that they allow exploration of, or learning from, data. This property has proved extremely valuable in the solution of many diverse problems in science and engineering. The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research. The application of some of the main algorithms is illustrated using case studies. The paper also attempts to give some indication as to which of the algorithms emerging now from the machine learning community are likely to be important for mechanical systems research in the future.",
keywords = "Condition monitoring, Identification, Machine learning, Natural computing, Soft computing, Structural health monitoring, System",
author = "Keith Worden and Staszewski, {Wieslaw J.} and Hensman, {James J.}",
year = "2011",
month = jan,
doi = "10.1016/j.ymssp.2010.07.013",
language = "English",
volume = "25",
pages = "4--111",
journal = "Mechanical Systems and Signal Processing",
issn = "0888-3270",
publisher = "Academic Press Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Natural computing for mechanical systems research

T2 - a tutorial overview

AU - Worden, Keith

AU - Staszewski, Wieslaw J.

AU - Hensman, James J.

PY - 2011/1

Y1 - 2011/1

N2 - A great many computational algorithms developed over the past half-century have been motivated or suggested by biological systems or processes, the most well-known being the artificial neural networks. These algorithms are commonly grouped together under the terms soft or natural computing. A property shared by most natural computing algorithms is that they allow exploration of, or learning from, data. This property has proved extremely valuable in the solution of many diverse problems in science and engineering. The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research. The application of some of the main algorithms is illustrated using case studies. The paper also attempts to give some indication as to which of the algorithms emerging now from the machine learning community are likely to be important for mechanical systems research in the future.

AB - A great many computational algorithms developed over the past half-century have been motivated or suggested by biological systems or processes, the most well-known being the artificial neural networks. These algorithms are commonly grouped together under the terms soft or natural computing. A property shared by most natural computing algorithms is that they allow exploration of, or learning from, data. This property has proved extremely valuable in the solution of many diverse problems in science and engineering. The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research. The application of some of the main algorithms is illustrated using case studies. The paper also attempts to give some indication as to which of the algorithms emerging now from the machine learning community are likely to be important for mechanical systems research in the future.

KW - Condition monitoring

KW - Identification

KW - Machine learning

KW - Natural computing

KW - Soft computing

KW - Structural health monitoring

KW - System

U2 - 10.1016/j.ymssp.2010.07.013

DO - 10.1016/j.ymssp.2010.07.013

M3 - Journal article

AN - SCOPUS:78349261500

VL - 25

SP - 4

EP - 111

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

IS - 1

ER -