Sunspot Forecasting by Using Chaotic Time-series Analysis and NARX Network

Jiang, Chuanjin; Song, Fugen

doi:10.4304/jcp.6.7.1424-1429

Cited by 24 publications

(18 citation statements)

References 7 publications

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“…The weights and biases update laws use the Levenberg-Marquardt optimization scheme [52] which minimizes a combination of the squared error of the estimated and actual values of the output and weights and then determines the optimal combination for minimizing a nonlinear performance index. These details are not included and can be found from [61]. It should be pointed out that the details corresponding to the training algorithm of the Elman neural network that is introduced in the next section are provided explicitly.…”

Section: Narx Neural Networkmentioning

confidence: 97%

Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis

Kiakojoori

Khorasani

2015

Neural Comput & Applic

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In this paper, the problem of health monitoring and prognosis of aircraft gas turbine engines is considered by using computationally intelligent methodologies. Two different dynamic neural networks, namely the nonlinear autoregressive with exogenous input neural networks and the Elman neural networks, are developed and designed for this purpose. The proposed dynamic neural networks are designed to capture the dynamics of two main degradations in the gas turbine engine, namely the compressor fouling and the turbine erosion. The health status and condition of the engine in terms of the turbine output temperature (TT) are then predicted subject to occurrence of these deteriorations. Various scenarios consisting of fouling and erosion separately as well as combined are considered. For each scenario, several neural networks are trained and their performance in predicting multiple flights ahead TTs is evaluated. Finally, the most suitable neural networks for achieving the best prediction are selected by using the normalized Bayesian information criterion model selection. Simulation results presented demonstrate and illustrate the effective performance of our proposed neural networkbased prediction and prognosis strategies.

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Section: Narx Neural Networkmentioning

confidence: 97%

Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis

Kiakojoori

Khorasani

2015

Neural Comput & Applic

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“…Nonlinear systems can be approximated by an MLP network, called NARX, a powerful dynamic model for time series prediction (Jiang and Song, 2011;Menezes Jr. and Barreto, 2008). The architecture of a NARX network based on a multilayer perceptron neural network consists of p antecedent values of exogenous input vectors X(t), such as online rainfall intensity; q antecedent actual values z(t + n − q), such as inundation depths, which are tapped-delay inputs or fed back from the model's output; and a single n-step-ahead outputẑ(t + n).…”

Section: Narx Networkmentioning

confidence: 99%

Online multistep-ahead inundation depth forecasts by recurrent NARX networks

Shen

Chang

2013

Hydrol. Earth Syst. Sci.

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Abstract. Various types of artificial neural networks (ANNs)have been successfully applied in hydrological fields, but relatively scant on multistep-ahead flood inundation forecasting, which is very difficult to achieve, especially when dealing with forecasts without regular observed data. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model at thirteen inundation-prone sites in Yilan County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit error growth and accumulation when being applied to online multistep-ahead inundation forecasts over a long lasting forecast period. For comparison, a feedforward time-delay and an online feedback configuration of NARX networks (T-NARX and O-NARX) were performed. The results show that (1) T-NARX networks cannot make online forecasts due to unavailable inputs in the constructed networks even though they provide the best performances for reference only; and (2) R-NARX networks consistently outperform O-NARX networks and can be adequately applied to online multistep-ahead forecasts of inundation depths in the study area during typhoon events.

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“…The network also provides faster convergences with better generalization than other networks. Recently, NARX networks have been applied for solving non-linear problems in various fields other than hydrological applications with remarkable results [28][29][30]. In the present study, the results of simultaneous forecasting on a river system obtained with NARX were used for comparison with the results obtained with RBFNN.…”

Section: Introductionmentioning

confidence: 96%

Application of RBFNNs Incorporating MIMO Processes for Simultaneous River Flow Forecasting

Tripura

Roy

Barbhuiya

2018

J. Eng. Technol. Sci.

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Simultaneous flow forecasting using multi-input multi-output (MIMO) processes is an efficient technique for accurate flow forecasting on river systems. The present study demonstrates the capability of radial basis function neural networks (RBFNN) incorporating MIMO processes in simultaneous river flow forecasting. The river system considered in the present study was the Barak river system, Assam, India. Hourly concurrent discharge data were collected from the Central Water Commission, Shillong, India from multiple sections of the Barak river system. The forecasts were tested for short-range time horizons, i.e. 1, 3, 6 and 12 hours in advance, and a comparative analysis was done using the popular Nonlinear Autoregressive with Exogenous Inputs (NARX) time series model. The result shows that MIMO-NARX provided higher prediction accuracy than MIMO-RBFNN, even at longer lead times when compared to following various statistical criterions.

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Sunspot Forecasting by Using Chaotic Time-series Analysis and NARX Network

Cited by 24 publications

References 7 publications

Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis

Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis

Online multistep-ahead inundation depth forecasts by recurrent NARX networks

Application of RBFNNs Incorporating MIMO Processes for Simultaneous River Flow Forecasting

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