Recurrent Multilayer Perceptron Network Research Articles

Learning in dynamic neural networks using signal flow graphs

The paper presents the universal approach to the determination of the sensitivity functions for dynamic neural networks and its application in learning algorithms of adaptive networks. The method is based on the application of signal flow graph and specially defined graph adjoint to it. The method is equally applied to either feed-forward or recurrent network structures. This paper is mainly concerned with neural network applications of the approach. Different kinds of dynamic neural networks are considered and discussed in the paper: the FIR dynamic multilayer perceptron (MLP), the cascade connection of dynamic MLPs as well as two non-linear recurrent systems: the dynamic recurrent MLP network and ARMA recurrent network. The rule of sensitivity determination has been applied in practical learning of neural networks. Chosen results of numerical experiments concerning the application of this approach to the learning processes of recurrent neural networks are also given and discussed.

Application of the recurrent multilayer perceptron in modeling complex process dynamics

A nonlinear dynamic model is developed for a process system, namely a heat exchanger, using the recurrent multilayer perceptron network as the underlying model structure. The perceptron is a dynamic neural network, which appears effective in the input-output modeling of complex process systems. Dynamic gradient descent learning is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over a static learning algorithm used to train the same network. In developing the empirical process model the effects of actuator, process, and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response of various testing sets. Extensive model validation studies with signals that are encountered in the operation of the process system modeled, that is steps and ramps, indicate that the empirical model can substantially generalize operational transients, including accurate prediction of instabilities not in the training set. However, the accuracy of the model beyond these operational transients has not been investigated. Furthermore, online learning is necessary during some transients and for tracking slowly varying process dynamics. Neural networks based empirical models in some cases appear to provide a serious alternative to first principles models.

Empirical Model Development and Validation with Dynamic Learning in the Recurrent Multilayer Perceptron

A nonlinear multivariable empirical model is developed for a U-tube steam generator using the recurrent multilayer perceptron network as the underlying model structure. The recurrent multilayer perceptron is a dynamic neural network, very effective in the input-output modeling of complex process systems. A dynamic gradient descent learning algorithm is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over static learning algorithms. In developing the U-tube steam generator empirical model, the effects of actuator, process,and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response. Extensive model validation studies indicate that the empirical model can substantially generalize (extrapolate), though online learning becomes necessary for tracking transients significantly different than the ones included in the training set and slowly varying U-tube steam generator dynamics. In view of the satisfactory modeling accuracy and the associated short development time, neural networks based empirical models in some cases appear to provide a serious alternative to firstmore » principles models. Caution, however, must be exercised because extensive on-line validation of these models is still warranted.« less

Nonlinear Identification of Process Dynamics Using Neural Networks

In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios.