Abstract
To many, artificial neural networks (ANNs) are the archetypal machine learning algorithm; they have a power and generality, together with a long pedigree, which makes them difficult to displace from this position. As one of the earliest general machine learners, ANNs were one of the first to be adopted for addressing data-based engineering problems. This chapter will discuss the historical development of ANNs in the context of engineering usage; in that context, it will prove useful to divide the history into three main periods: pre-history, the first (MLP) age, and the second (deep) age.
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The authors would like to acknowledge the support of the UK EPSRC via the Programme Grants EP/R006768/1 and EP/R004900/1. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC-BY-ND) licence to any Author Accepted Manuscript version arising.
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Worden, K., Tsialiamanis, G., Cross, E.J., Rogers, T.J. (2023). Artificial Neural Networks. In: Rabczuk, T., Bathe, KJ. (eds) Machine Learning in Modeling and Simulation. Computational Methods in Engineering & the Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-36644-4_2
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