Elman neural networks for characterizing voids in welded strips: a study

Cacciola, Matteo; Megali, Giuseppe; Pellicanó, Diego; Morabito, Francesco Carlo

doi:10.1007/s00521-011-0609-3

Cited by 23 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The neurons contained in each layer are used to propagate information from one layer to another. The dynamics of the change in hidden state neuron activations in the context layer is as follows [30,31]:…”

Section: Elman Neural Network (Enn)mentioning

confidence: 99%

See 1 more Smart Citation

Forecasting wind speed using empirical mode decomposition and Elman neural network

Wang

Zhang

et al. 2014

Applied Soft Computing

238

View full text Add to dashboard Cite

Section: Elman Neural Network (Enn)mentioning

confidence: 99%

“…This ability makes them applicable to time series prediction with satisfactory prediction results [29]. As a special recurrent neural network, the Elman neural network (ENN) has been widely used in the field of time series prediction [30,31]. However, ENN is rarely used for wind speed prediction.…”

Section: Introductionmentioning

confidence: 99%

Forecasting wind speed using empirical mode decomposition and Elman neural network

Wang

Zhang

et al. 2014

Applied Soft Computing

238

View full text Add to dashboard Cite

“…An ENN consists of four layers: input, hidden, output and feedback layers. The trait of time-delay memory increases the sensitivity of the ENN to historical data, a local feedback network internally enhances the capacity of the ENN to address dynamic information, and these together give ENNs an advantage over static neural networks in modeling hydraulic systems [39], [40].…”

Section: A Elman Neural Network (Enn)mentioning

confidence: 99%

A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation

2020

View full text Add to dashboard Cite

Wind speed forecasting is an essential procedure in electric grid dispatching. Short-term wind speed forecasting can be a great challenge and an intractable issue in increasing wind energy output and guaranteeing power safety. The current wind speed forecasting models are based on a single model, which is generally an artificial intelligence model or a statistical forecasting method. However, these models cannot perform well in all cases. An effective combined model is proposed in this paper, and this model includes four parts: weight coefficient optimization calculation based on the nonpositive constraint combination theory, singular-spectrum analysis, combined forecasting and discussion of results. The developed model can decrease the negative influences of the component models and maximize the advantages of each component model. To evaluate the forecasting accuracy of our proposed model, ten minutes of wind speed data from Shandong Peninsula, China, were used as test cases. It is clearly demonstrated that the developed combined strategy outperforms the individual forecasting methods in terms of forecast performance and stability. INDEX TERMS Combined model, data preprocessing approach, nonpositive constraint combination theory, weight coefficient optimization.

show abstract

“…As is well known, a recurrent network has some advantages, such as having time series and nonlinear prediction capabilities, faster convergence, and more accurate mapping ability. References [ 25 , 26 ] combine Elman neural network with different areas for their purposes. In this network, the outputs of the hidden layer are allowed to feedback onto themselves through a buffer layer, called the recurrent layer.…”

Section: Proposed Approachmentioning

confidence: 99%

Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

Wang

Wen

et al. 2016

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.

show abstract

Elman neural networks for characterizing voids in welded strips: a study

Cited by 23 publications

References 13 publications

Forecasting wind speed using empirical mode decomposition and Elman neural network

Forecasting wind speed using empirical mode decomposition and Elman neural network

A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation

Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

Contact Info

Product

Resources

About