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Natural computing for mechanical systems research: A tutorial overview

机译:机械系统研究的自然计算:教程概述

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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.
机译:在过去的半个世纪中开发的许多计算算法都是由生物系统或过程所激发或提出的,其中最著名的是人工神经网络。这些算法通常在软计算或自然计算下分组在一起。大多数自然计算算法共有的一个特性是,它们允许对数据进行探索或从中学习。在解决科学和工程学中许多不同问题的过程中,此特性已被证明极其有价值。本文旨在作为自然计算最基本方法的基础理论的教程概述,因为它们被应用在机械系统研究中。通过案例研究说明了一些主要算法的应用。本文还试图给出一些指示,说明机器学习社区中现今涌现出的哪些算法可能对未来的机械系统研究很重要。

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