Precise and reliable monthly runoff prediction plays a vital role in the optimal management of water resources, but nonstationarity skewness time series can pose major challenges for developing appropriate models. To address these issues, this paper proposes novel hybrid model by introducing variational mode decomposition (VMD) Box–Cox transformation (BC) into Elman neural network (Elman), name...

This paper introduces a kind of diagnosis principle and learning algorithm of steam turbine fault diagnosis which based on Elman neural network. Comparing the results of the Elman neural network and the traditional BP neural network diagnosis, the results shows that Elman neural network is an effective way to improve the learning speed , effectively suppress the minimum defects that the traditi...

The broad context of this study is the investigation of the nature of computation in recurrent networks (RNs). The current study has two parts. The first is to show that a RN can solve a problem that we take to be of interest (a counting task), and the second is to use the solution as a platform for developing a more general understanding of RNs as computational mechanisms. We begin by presenti...

— For predicting the shelf life of processed cheese stored at 7-8º C, Elman single and multilayer models were developed and compared. The input variables used for developing were applied in order to compare the prediction ability of the developed models. The Elman models got simulated very well and showed excellent agreement between the experimental data and the predicted values, suggesting tha...

Multilayer perceptron network (MLP), FIR neural network and Elman neural network were compared in four different time series prediction tasks. Time series include load in an electric network series, fluctuations in a far-infrared laser series, numerically generated series and behaviour of sunspots series. FIR neural network was trained with temporal backpropagation learning algorithm. Results s...

Original Elman, which is one of the well-known dynamic recurrent neural network (DRNN), has been improved to easily apply in dynamic systems identification during the past decade. In this paper, a learning algorithm for Original Elman neural networks (ENN) based on modified particle swarm optimization (MPSO), which is a swarm intelligent algorithm (SIA), is presented. MPSO and Elman are hybridi...

Network latency is a crucial factor affecting the quality of communications networks due to irregularity vehicular traffic. To address problem performance degradation or instability caused by in networks, this paper proposes time delay prediction algorithm, which digital twin technology employed obtain large quantity actual data for and verify autocorrelation. Subsequently, meet conditions ARMA...

In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-...

Introduction Simple recurrent networks (SRNs) are able to learn and represent lexical classes (Elman, 1990) and grammatical knowledge, such as agreement and argument structure (Elman, 1991), on the basis of co-occurrence regularities embedded in simple and complex sentences. In the present study, we address the question whether SRNs can represent differences in the thematic roles assigned by ve...

The operational life of rotating machines has to be extended using a predictive condition maintenance tool. Among various condition monitoring techniques, vibration analysis is most widely used technique in industry. Signals are extracted for evaluating the condition of machine; further diagnostics is carried out with detected signals to extend the life of machine. With help of detected signals...