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Statistical and optimization methods to expedite neural network training for transient identification

Conference ·
OSTI ID:6587226
 [1];  [2];  [3]
  1. Argonne National Lab., IL (United States). Reactor Analysis Div.
  2. Universidad Nacional Autonoma de Mexico, Mexico City (Mexico). Inst. de Ciencias Nucleares
  3. Michigan Univ., Ann Arbor, MI (United States). Dept. of Nuclear Engineering
+ Show Author Affiliations

Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network.

Research Organization:
Argonne National Lab., IL (United States). Reactor Analysis Div.
Sponsoring Organization:
DOE; USDOE, Washington, DC (United States)
DOE Contract Number:
W-31109-ENG-38
OSTI ID:
6587226
Report Number(s):
ANL/RA/CP-77255; CONF-930401--12; ON: DE93009985
Country of Publication:
United States
Language:
English

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Related Subjects

22 GENERAL STUDIES OF NUCLEAR REACTORS
220900* -- Nuclear Reactor Technology-- Reactor Safety
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
ALGORITHMS
CONVERGENCE
EDUCATION
MATHEMATICAL LOGIC
MATHEMATICAL MODELS
NEURAL NETWORKS
NONLINEAR PROBLEMS
NUCLEAR FACILITIES
NUCLEAR POWER PLANTS
OPTIMIZATION
POWER PLANTS
STATISTICAL MODELS
THERMAL POWER PLANTS
TRAINING
TRANSIENTS