Empirical model development and validation with dynamic learning in the recurrent multilayer perception

Journal Article · Tue Feb 01 00:00:00 EST 1994 · Nuclear Technology; (United States)

OSTI ID:5128958

Parlos, A G; Chong, K T; Atiya, A F ^[1]

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1. Texas A and M Univ., College Station, TX (United States). Dept. of Nuclear Engineering

A nonlinear multivariable empirical model is developed for a U-tube steam generator using the recurrent multilayer perceptron network as the underlying model structure. The recurrent multilayer perceptron is a dynamic neural network, very effective in the input-output modeling of complex process systems. A dynamic gradient descent learning algorithm is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over static learning algorithms. In developing the U-tube steam generator empirical model, the effects of actuator, process,and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response. Extensive model validation studies indicate that the empirical model can substantially generalize (extrapolate), though online learning becomes necessary for tracking transients significantly different than the ones included in the training set and slowly varying U-tube steam generator dynamics. In view of the satisfactory modeling accuracy and the associated short development time, neural networks based empirical models in some cases appear to provide a serious alternative to first principles models. Caution, however, must be exercised because extensive on-line validation of these models is still warranted.

• OSTI ID: 5128958
• Journal Information: Nuclear Technology; (United States), Vol. 105:2; ISSN 0029-5450
• Country of Publication: United States
• Language: English

Related Subjects

22 GENERAL STUDIES OF NUCLEAR REACTORS
NUCLEAR POWER PLANTS
REACTOR CONTROL SYSTEMS
NEURAL NETWORKS
STEAM GENERATORS
MATHEMATICAL MODELS
MULTIVARIATE ANALYSIS
REACTOR INSTRUMENTATION
BOILERS
CONTROL SYSTEMS
MATHEMATICS
NUCLEAR FACILITIES
POWER PLANTS
STATISTICS
THERMAL POWER PLANTS
VAPOR GENERATORS
220400* - Nuclear Reactor Technology- Control Systems