Dynamic gradient descent learning algorithms for enhanced empirical modeling of power plants

Conference · Fri Nov 01 04:00:00 EST 1991 · Transactions of the American Nuclear Society; (United States)

OSTI ID:5074299

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

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1. Texas A and M Univ., College Station (United States)

A newly developed dynamic gradient descent-based learning algorithm is used to train a recurrent multilayer perceptron network for use in empirical modeling of power plants. The two main advantages of the proposed learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation, instead of one forward and one backward pass of the backpropagation algorithm. The latter advantage results in computational time saving because both passes can be performed simultaneously. The dynamic learning algorithm is used to train a hybrid feedforward/feedback neural network, a recurrent multilayer perceptron, which was previously found to exhibit good interpolation and extrapolation capabilities in modeling nonlinear dynamic systems. One of the drawbacks, however, of the previously reported work has been the long training times associated with accurate empirical models. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm are demonstrated by a case study of a steam power plant. The number of iterations required for accurate empirical modeling has been reduced from tens of thousands to hundreds, thus significantly expediting the learning process.

• OSTI ID: 5074299
• Report Number(s): CONF-911107--
• Conference Information: Journal Name: Transactions of the American Nuclear Society; (United States) Journal Volume: 64
• Country of Publication: United States
• Language: English

Related Subjects

22 GENERAL STUDIES OF NUCLEAR REACTORS
220100* -- Nuclear Reactor Technology-- Theory & Calculation
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
COMPUTERIZED SIMULATION
DYNAMIC PROGRAMMING
FEEDBACK
ITERATIVE METHODS
KINETICS
KNOWLEDGE BASE
LEARNING
NEURAL NETWORKS
NUCLEAR FACILITIES
NUCLEAR POWER PLANTS
OPERATION
POWER PLANTS
PROGRAMMING
REACTOR KINETICS
REACTOR OPERATION
SIMULATION
THERMAL POWER PLANTS