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	Wavelet shrinkage data processing for neural networks in bioprocess modeling
	Chen, B. H. ; Woodley, J. M. ; Chen BH(陈秉辉)
	2002
关键词	artificial neural networks wavelet shrinkage bioprocess modeling
英文摘要	The modeling of biological systems has now become an essential prerequisite for effective bioprocess design, optimization and analysis. The difficulties present in using conventional techniques to model such a complex system make the application of artificial neural networks (ANNs) to these problems particularly attractive because of their capability for nonlinear mapping and lack of necessity for detailed mechanistic knowledge. However, building a reliable ANN model requires sufficient training data, which may be difficult when data are collected from litre-scale experiments. In this work, a bioconversion (with only limited experimental data) was firstly modeled by a radial basis function (RBF) neural network. Although the model provided a very low variance between experiment and simulation, it tended to result in oscillatory behaviour, which clearly does not reflect the accurate profile of the reaction. In order to overcome this drawback, wavelet shrinkage with biorthogonal filters was used to generate a reconstructed function using the RBF model as a base. The synthesis of N-acetyl-D-neuraminic acid by the enzymatic condensation of pyruvate with N-acetyl-D-mannosamine was used as a case study to show the effectiveness of the approach. The effects of alternative filters and border distortion are also discussed. (C) 2002 Elsevier Science Ltd. All rights reserved.
语种	英语
内容类型	期刊论文
源URL	[http://dspace.xmu.edu.cn/handle/2288/64206]
专题	化学化工－已发表论文
推荐引用方式 GB/T 7714	Chen, B. H.,Woodley, J. M.,Chen BH. Wavelet shrinkage data processing for neural networks in bioprocess modeling[J],2002.
APA	Chen, B. H.,Woodley, J. M.,&陈秉辉.(2002).Wavelet shrinkage data processing for neural networks in bioprocess modeling..
MLA	Chen, B. H.,et al."Wavelet shrinkage data processing for neural networks in bioprocess modeling".(2002).

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