Hierarchical Process Neural Network Model within Variable-Sampling Time

Abstract:

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The biochemical processes are usually described as seriously time varying and nonlinear dynamic systems. It is very costly and difficult to build their first-principle models due to the absence of inherent mechanism and efficient on-line sensors. In this paper, a hierarchical process neural network (HPNN) model within variable-sampling time has been proposed. Simulation is based on penicillin fed-batch fermentation process, shows that the model established is more accurately and efficient, and suffice for the requirements of control and optimization for biochemical processes.

Info:

Periodical:

Edited by:

Qi Luo

Pages:

920-925

DOI:

10.4028/www.scientific.net/AMM.20-23.920

Citation:

L. Chen and F. Liu, "Hierarchical Process Neural Network Model within Variable-Sampling Time", Applied Mechanics and Materials, Vols. 20-23, pp. 920-925, 2010

Online since:

January 2010

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$35.00

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