Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions

Huang, Yuansheng; Wang, Hongwei; Liu, Hui; Liu, Shijian

doi:10.1080/21642583.2019.1620655

Cited by 13 publications

(6 citation statements)

References 17 publications

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“…134,136,137,146,147,149,155 Water quality and demand were also predicted by using TCN and ENN in Refs. 140,153 An application of LSTMbased neural networks for correlated time series prediction was also proposed by Wan et al 143 Further, carbon dioxide emissions, 139 flood, 143 or NH 3 concentration for swine house 199 were also predicted by using deep-learning techniques, in particular ENN. 5.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyperparameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

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Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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“…Computational socioeconomics is an emerging interdisciplinary research direction that uses advanced tools to analyse large-scale real data, aiming to accurately and timely perceive the socioeconomic state and reveal and understand the laws of socioeconomic operation [9]. It uses complex networks to portray the interactions in socioeconomic systems and analyse the spatial structure and dynamics of socioeconomics to provide deeper insight into socioeconomic phenomena.…”

Section: Status Of Researchmentioning

confidence: 99%

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Dai

Lin

2021

Complexity

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In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm (IGA) based on elite heuristic operation and migration strategy and the improved coyote optimization algorithm (ICOA) based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation (BP) neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search (NAS), and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology.

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“…At present, the Elman network is widely used in various fields, and has achieved notable successful prediction results. [22][23][24][25][26] Sometimes, the prediction effect of a single model is not ideal, in order to further improve the prediction accuracy, many studies adopt the combined model prediction method, [27][28][29] the combined model can absorb the advantages of two or more methods so as to achieve a higher prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China

et al. 2021

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ObjectivesKashgar, located in Xinjiang, China has a high incidence of tuberculosis (TB) making prevention and control extremely difficult. In addition, there have been very few prediction studies on TB incidence here. We; therefore, considered it a high priority to do prediction analysis of TB incidence in Kashgar, and so provide a scientific reference for eventual prevention and control.DesignTime series study.Setting Kashgar, ChinaKashgar, China.MethodsWe used a single Box-Jenkins method and a Box-Jenkins and Elman neural network (ElmanNN) hybrid method to do prediction analysis of TB incidence in Kashgar. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the prediction accuracy.ResultsAfter careful analysis, the single autoregression (AR) (1, 2, 8) model and the AR (1, 2, 8)-ElmanNN (AR-Elman) hybrid model were established, and the optimal neurons value of the AR-Elman hybrid model is 6. In the fitting dataset, the RMSE, MAE and MAPE were 6.15, 4.33 and 0.2858, respectively, for the AR (1, 2, 8) model, and 3.78, 3.38 and 0.1837, respectively, for the AR-Elman hybrid model. In the forecasting dataset, the RMSE, MAE and MAPE were 10.88, 8.75 and 0.2029, respectively, for the AR (1, 2, 8) model, and 8.86, 7.29 and 0.2006, respectively, for the AR-Elman hybrid model.ConclusionsBoth the single AR (1, 2, 8) model and the AR-Elman model could be used to predict the TB incidence in Kashgar, but the modelling and validation scale-dependent measures (RMSE, MAE and MAPE) in the AR (1, 2, 8) model were inferior to those in the AR-Elman hybrid model, which indicated that the AR-Elman hybrid model was better than the AR (1, 2, 8) model. The Box-Jenkins and ElmanNN hybrid method therefore can be highlighted in predicting the temporal trends of TB incidence in Kashgar, which may act as the potential for far-reaching implications for prevention and control of TB.

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Elman neural network optimized by firefly algorithm for forecasting China's carbon dioxide emissions

Cited by 13 publications

References 17 publications

Deep Learning for Time Series Forecasting: A Survey

Deep Learning for Time Series Forecasting: A Survey

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China

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