Improving learning of neural networks for nuclear power plant transient classification
The backpropagation learning algorithm has proven to be a robust method for training feedforward multilayer neural networks to map the relationships between input/output patterns. However, as with many gradient descent optimization methods, the rate of convergence of the backpropagation algorithm decreases the closer it gets to the solution, and it requires judicious selection of the learning and momentum constants to achieve reasonable convergence and avoid oscillations about the optimum solution. In this paper, the discussion focuses on how the method of conjugate gradients can be combined with the backpropagation algorithm to improve and accelerate learning in neural networks and eliminate the process of selecting parameters. The proposed method was used to train a neural network to classify nuclear power plant transients, and it significantly expedited the learning process. 5 refs., 1 fig.
- OSTI ID:
- 6659218
- Report Number(s):
- CONF-921102--
- Journal Information:
- Transactions of the American Nuclear Society; (United States), Journal Name: Transactions of the American Nuclear Society; (United States) Vol. 66; ISSN 0003-018X; ISSN TANSAO
- Country of Publication:
- United States
- Language:
- English
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Statistical and optimization methods to expedite neural network training for transient identification
Statistical and optimization methods to expedite neural network training for transient identification
Related Subjects
220900* -- Nuclear Reactor Technology-- Reactor Safety
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
ACCIDENTS
ALGORITHMS
CONTROL SYSTEMS
MATHEMATICAL LOGIC
NEURAL NETWORKS
NUCLEAR FACILITIES
NUCLEAR POWER PLANTS
POWER PLANTS
REACTOR ACCIDENTS
REACTOR CONTROL SYSTEMS
REACTOR SAFETY
SAFETY
THERMAL POWER PLANTS
TRANSIENTS