A delay damage model selection algorithm for NARX neural networks

Lin, Tsung-Nan; Giles, C. Lee; Horne, Bill G.; Kung, Sun‐Yuan

doi:10.1109/78.650098

Cited by 111 publications

(21 citation statements)

References 49 publications

(63 reference statements)

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“…It was shown that this type of neural networks is a powerful type of neural networks for modeling of nonlinear systems. Also in comparison with to the other types of neural networks, learning in NARX structure is more effective and in general, this type of learning algorithm converges faster than other types [10], [13].…”

Section: Structurementioning

confidence: 96%

“…The base architecture of RNN for the proposed method to predict chaotic behavior of fractional-order systems is considered as a Nonlinear Autoregressive with eXogeneous inputs (NARX) neural networks, in this paper [10].…”

Section: Structurementioning

confidence: 99%

“…In this type of network as shown in Fig. (1), the feedback of system is defined using the system output and also independent input and there is not any feedback from outputs of hidden layer of network, nevertheless output of NARX networks more accurate than other types of recurrent neural networks [10]. Basic structure of this model is shown in Fig.…”

Section: Structurementioning

confidence: 99%

See 2 more Smart Citations

Prediction of Fractional-Order Chaotic Systems Behavior Using NARX Neural Networks

Behinfaraz¹,

Ghaemi²,

Khanmohammadi³

2018

IJCEE

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Chaotic systems are complex systems which have very different behavior in different situation. When a chaotic system is fractional-order type, this complexity become more than past. Then prediction of these systems behavior is an important task which has many application in many research field. In this paper we propose a method based on artificial neural networks to prediction of this systems behavior in different conditions. Finally simulation examples show the performance of the proposed method.

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Section: Structurementioning

confidence: 96%

Section: Structurementioning

confidence: 99%

Section: Structurementioning

confidence: 99%

See 1 more Smart Citation

Prediction of Fractional-Order Chaotic Systems Behavior Using NARX Neural Networks

Behinfaraz¹,

Ghaemi²,

Khanmohammadi³

2018

IJCEE

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show abstract

“…On the other side, NARXNN involves two tapped-delay lines from the input-output signals [34]. The exogenous input values are integrated into the parametric equation of NARXNN as [35]:…”

Section: A Narx Neural Networkmentioning

confidence: 99%

“…The output of the NARXNN, y(n), is fed back to the input of the network (with a delay). Two different modes for the training of NARXNN model are depicted [35]:…”

Section: A Narx Neural Networkmentioning

confidence: 99%

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

et al. 2021

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This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) with Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. The performance of the proposed PVPF technique was investigated using two real datasets with different scales and locations. The comparative analysis of the NARX-LSTM with twelve existing benchmarks confirms its superiority in terms of accuracy measures. In summary, the proposed NARX-LSTM technique has the following major achievements: 1) Improves the prediction performance of the original LSTM and NARXNN models; 2) Evaluates the uncertainties associated with point forecasts with high accuracy; 3) Provides a high generalization capability for PV systems with different scales. Numerical results of the comparison of the proposed NARX-LSTM method with two real-world PV systems in Australia and USA demonstrate its improved prediction accuracy, outperforming the benchmark approaches with an overall normalized Rooted Mean Squared Error (nRMSE) of 1.98% and 1.33% respectively. INDEX TERMS Long Short-Term Memory (LSTM), photovoltaic power forecasting, Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN), Tabu Search Algorithm (TSA).

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A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Cui

Wang

et al. 2021

Intl J of Energy Research

215

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Implementing carbon neutrality and emission peak policies requires a highlevel electric vehicle field. Lithium-ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium-ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium-ion batteries using neural network methods, the methods are classified into three categories: feed-forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next-generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles.

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A delay damage model selection algorithm for NARX neural networks

Cited by 111 publications

References 49 publications

Prediction of Fractional-Order Chaotic Systems Behavior Using NARX Neural Networks

Prediction of Fractional-Order Chaotic Systems Behavior Using NARX Neural Networks

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

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