Identification of the appropriate architecture of multilayer feed-forward neural network for estimation of NPPs parameters using the GA in combination with the LM and the BR learning algorithms

摘要

In this study, accurate estimation of nuclear power plant (NPP) parameters is done using the new and simple technique. The proposed technique using the genetic algorithm (GA) in combination with the Bayesian regularization (BR) and Levenberg- Marquardt (LM) learning algorithms identifies the appropriate architecture for estimation of the target parameters. In the first step, the input patterns features are selected using the features selection (FS) technique. In the second step, the appropriate number of hidden neurons and hidden layers are investigated to provide a more efficient initial population of the architectures. In the third step, the estimation of the target parameter is done using different architectures of multilayer feed-forward neural network (MFFN) with LM learning algorithm in which the maximum number of hidden neurons and the maximum number of hidden layers have been limited. In the fourth step, the proposed technique using the GA in combination with the BR learning algorithm is proposed to determine the more appropriate number and the more appropriate distribution of hidden neurons. To study the performance of the proposed technique, Bushehr nuclear power plant (BNPP) transients are examined. The different important transients/parameters are estimated. The results of the estimations by the identified architecture in comparison with the other appropriate architectures show superiority of the proposed technique. Therefore, the proposed technique can be used reliably for accurate estimation of the important parameters and can be used as a support tool by the operators in confront with transients. (C) 2021 Elsevier Ltd. All rights reserved.