A comparison of different neural network topologies : the modelling of the high and low pressure compressors of the PBMR

Abstract

A reliable and practical method of modelling nonlinear dynamic systems is essential. Traditionally these systems have been modelled by the use of parameterised models. An alternative available to model nonlinear dynamic systems is artificial neural networks. Artificial neural networks are powerful empirical modelling tools that can be trained to mimic complicated multi-input, multi-output nonlinear dynamic systems. The system that is investigated for the modelling purpose is the Pebble Bed Modular Reactor, or more specifically the high and low pressure compressors of the Pebble Bed Micro Model. In order to utilize the best neural networks topology and configuration, neural networks were compared. In the comparison of the neural networks, the execution time, final error, maximum amplitude error, number of epochs used and convergence speed were investigated. All these variables were compared for both time-delayed feedforward and recurrent networks. The learning algorithms used to train the neural networks include the Levenberg-Marquardt, resilient back-propagation, Broyden-Fletcher-Goldfarb-Shanno quasi-Newton, one step secant, gradient descent and gradient descent with momentum algorithms. The neural networks were successfully applied on both the high and low pressure compressors and a high level of modelling accuracy was obtained in all the test instances. The applied Levenberg-Marquardt algorithm, in conjunction with the appropriate network topology, presents the optimal results. This study showed that neural networks provide a fast, stable and accurate method of modelling nonlinear dynamic systems and provide a viable alternative to existing methods for modelling nonlinear dynamic systems. The results obtained showed that the accuracy and performance of the different topologies used, is directly subjective to the complexity of the system being modelled. The methodology that is used can also be applied to any linear or nonlinear, static or dynamic system.