Natural computing for mechanical systems research: A tutorial overview

Keith Worden

doi:10.1016/j.ymssp.2010.07.013

Return

Natural computing for mechanical systems research: A tutorial overview

2011-01-01

225

PRE

Keith Worden *

Wiesław J. Staszewski

James Hensman

DOI：10.1016/j.ymssp.2010.07.013

Original

Original request for help

Save

Abstract

En 中文

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. (C) 2010 Elsevier Ltd. All rights reserved.

Keywords:

Natural computing

Soft computing

Machine learning

System

Identification

Condition monitoring

Structural health monitoring

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.