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Title: Nonlinear identification of process dynamics using neural networks

Journal Article · · Nuclear Technology; (United States)
OSTI ID:5776510
; ;  [1];  [2]
  1. Texas A and M Univ., College Station, TX (United States). Dept. of Nuclear Engineering
  2. Univ. of California-Irvine, Dept. of Electrical and Computer Engineering, Irvine, CA (US)

In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios.

OSTI ID:
5776510
Journal Information:
Nuclear Technology; (United States), Vol. 97:1; ISSN 0029-5450
Country of Publication:
United States
Language:
English