NDLI: Application of neural networks to empirical (vs. model) data: a diffusion tube experiment sample (white blood cells application)

Content Provider	IEEE Xplore Digital Library
Author	Thomas, M.M.
Copyright Year	1992
Description	Author affiliation: Dept. of Chem. Eng., Washington Univ., St. Louis, MO, USA (Thomas, M.M.)
Abstract	The author describes the Mackey-Glass chaotic time series equations (1977) and the artificial neural networks that have been applied to (one of) them. The networks describe the time series model with sufficient accuracy. The question of whether the networks describe the corresponding physical phenomena with enough accuracy is then raised. A diffusion tube experiment to determine the gaseous diffusion coefficient of two components (CO/sub 2/ and air) and the experiment's corresponding analytical model are described. Artificial neural networks of 3-8-1 architecture are trained on empirical data from the diffusion tube system. The resulting network output, though extrapolated, is in the same accuracy range as the corresponding analytical model. A discussion of these results is included for the density of circulating mature white blood cells in a chronic granulocytic leukemia (CGL) patient.
Starting Page	522
Ending Page	527
File Size	358731
Page Count	6
File Format	PDF
ISBN	0780305590
DOI	10.1109/IJCNN.1992.226935
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-06-07
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Artificial neural networks White blood cells Chaos Analytical models Neurons Differential equations Delay effects Backpropagation Chemical engineering
Content Type	Text
Resource Type	Article

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