Montague G, Morris J
Department of Chemical and Process Engineering, University of Newcastle, Newcastle upon Tyne, UK.
Trends Biotechnol. 1994 Aug;12(8):312-24. doi: 10.1016/0167-7799(94)90048-5.
In the mid-1980s, widespread interest in research into artificial neural networks re-emerged following a period of reduced research funding. The much wider availability and the increased power of computing systems, together with new areas of research, is expanding the range of potential application. The main reason for this is that the potential to describe the characteristics of extremely complex systems accurately has been attributed to this methodology. This article examines the contribution of various network methodologies to bioprocess modelling, control and pattern recognition. Industrial processes can benefit from the application of feedforward networks with sigmoidal activation functions, radial basis function networks and autoassociative networks. The contribution that neural networks can make to biochemical and microbiological scientific research is also reviewed briefly.
20世纪80年代中期，在研究资金减少一段时间后，对人工神经网络研究的广泛兴趣再度兴起。计算系统更广泛的可用性和更强的计算能力，以及新的研究领域，正在扩大潜在应用的范围。主要原因在于，这种方法被认为具有准确描述极其复杂系统特征的潜力。本文探讨了各种网络方法在生物过程建模、控制和模式识别方面的贡献。工业过程可以从具有S型激活函数的前馈网络、径向基函数网络和自联想网络的应用中受益。本文还简要回顾了神经网络对生物化学和微生物学科学研究的贡献。
