The prediction model of worsted yarn quality based on CNN–GRNN neural network

Hu, Zhenlong; Zhao, Qiang; Wang, Jun

doi:10.1007/s00521-018-3723-7

Cited by 22 publications

(11 citation statements)

References 13 publications

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“…Hu et al (2019) predicted China’s carbon emissions using the Firefly Algorithm-optimized Elman neural network. They discovered that Elman could provide more excellent performance than GRNN [ 50 ].…”

Section: Resultsmentioning

confidence: 99%

“…In a word, the Elman neural network has superior performance in carbon emission prediction and is more suitable for the issue in this study, because the above results demonstrate that the Elman prediction model has the best effect. [50].…”

Section: Performance Evaluation Of Different Carbon Emission Prediction Modelsmentioning

confidence: 99%

“…analyzed and predicted NH 3 concentration in atmosphere. They found that Elman performed much better in accuracy than BPNN[49] Hu et al (2019). predicted China's carbon emissions using the Firefly Algorithm-optimized Elman neural network.…”

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confidence: 99%

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Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Jin

2021

PLoS ONE

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Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become a vital issue of general concern in the current society. In this study, the carbon emission data of Chinese provinces in 1999–2019 are collected and analyzed, so as to identify the carbon emission of direct consumption per 10,000 residents in each province (including each municipal city and autonomous region) and the entire nation based on population data. The Arc Geographic Information Science Engine (ArcGIS Engine) and C#.NET platform are employed to call the MATLAB neural network toolbox. A model is selected and embedded in the prediction system to develop the entire system. This study demonstrates that the carbon emissions per resident in Northern China are significantly higher than those in Southern China, with the rate of carbon emissions continuing to increase over time. Compared with other models, the Elman neural network has a higher carbon emission prediction accuracy, but with more minor errors. For instance, its accuracy and prediction performance are improved by 55.93% and 19.48%, respectively, compared with the Backpropagation Neural Network (BPNN). The prediction results show that China is expected to reach its peak carbon emission in around 2025–2030. The above results are acquired based on the concept of carbon emissions and neural network model theories, supported by GIS component technology and intelligent methods. The feasibility of BPNN, Radial Basis Function (RBF) and Elman neural network models for predicting residential carbon emissions is analyzed. This study also designs a comprehensive, integrated and extensible visual intelligent platform, which is easy to implement and stable in operation. The trend and characteristics of carbon emission changes from 2027 to 2032 are explored and predicted based on the data about direct carbon emissions of Chinese provincial residents from 1999 to 2019, purposed to provide a scientific basis for the control and planning of carbon emissions.

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Section: Resultsmentioning

confidence: 99%

Section: Performance Evaluation Of Different Carbon Emission Prediction Modelsmentioning

confidence: 99%

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Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Jin

2021

PLoS ONE

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“…There is strong curve mapping ability, flexible network structure, high fault tolerance, and fast learning speed in the algorithm. It has been applied in the construction of multiple network models [23]. The neural network can get a better prediction effect under the premise of a few samples.…”

Section: Grnn (Generalized Regression Neural Network) Is a Neural Netmentioning

confidence: 99%

Prediction of Direct Carbon Emissions of Chinese Provincial Residents under Artificial Neural Networks in Deep Learning Environment

Jin

2020

Preprint

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It is aimed to deepen the understanding of the consumption carbon emissions of Chinese provinces, establish an accurate and feasible carbon emission prediction model, develop an urban low-carbon economy, and ensure the sustainable development of Chinese cities. Through the national statistical data information, based on the artificial neural network model, mathematical statistics and deep learning methods are used to learn and analyze the carbon emission data of various provinces in China from 1999 to 2019. The neural network toolbox in Matlab is used to program separately to realize the prediction of carbon emissions by different neural network models. After comparing and analyzing the accuracy and prediction performance, the optimal model for the prediction effect is selected. Finally, based on ArcGIS Engine (Arc Geographic Information Science Engine) and C#.NET platform, the call to Matlab neural network toolbox is realized. The selected model is embedded in the prediction system to complete the development of the entire system. The results show that the carbon emissions of residents in the north are distinctly higher than those in the south. Also, with the passage of time, the rate of carbon emissions continues to accelerate. Compared with other models, Elman neural network has higher accuracy and smaller error in carbon emission prediction. Compared to BP (Back Propagation) neural network, the accuracy is improved by 55.93%, and the prediction performance is improved by 19.48%. The prediction results show that China is expected to reach the peak of carbon emissions from 2027 to 2032. This investigation will provide a theoretical basis to control and plan carbon emissions from Chinese urban residents.

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“…Dong et al [23] proposed a method to evaluate the degree of emotion being motivated in continuous music videos based on asymmetry index (AsI). Zhao et al [24] proposed a new neural network; it combines convolutional neural network (CNN) with general regression neural network (GRNN), which is written as the CNN-GRNN. The sparse subspace clustering (SSC) algorithm is introduced by Wang et al [25] to analyze the time series data.…”

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confidence: 99%

Special issue on emergence in human-like intelligence toward cyber-physical systems

Yen

2019

Neural Comput & Applic

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As the famous slogan ''Connecting People'' indicates, a lot of the developments in novel technologies intensify the relationship between people without necessarily enhancing technologies that are close to the nature of human beings. Examples can be easily found in recent computing paradigms, such as Cloud Computing that advances network infrastructure for data storage and resource sharing, or the Internet of Things that investigates the intelligence and awareness of objects involved in the network. From this point of view, the existing state-of-the-art solutions, in realms of artificial intelligence and/or computational intelligence, significantly differ from the human competence level in many research domains. Even though it is generally not clear whether human-like approach would show its upper-hand over existing methods, the exploration of this research path seems to be advantageous and challenging. Human-like intelligence is arguably one of the most powerful approaches for the optimization and design of complex Web, as well as its correlated issues so as to meet the requirements and growing needs of new challenging applications (and services as well). Although the state of the art behind the computational intelligence has already reached an impressive scale, there remain quite a lot of attractive research topics, with consideration of human-like or human-oriented factors, for further and promising developments.The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. This special issue will be published by Neural Computing and Applications as special issues.

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The prediction model of worsted yarn quality based on CNN–GRNN neural network

Cited by 22 publications

References 13 publications

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks

Prediction of Direct Carbon Emissions of Chinese Provincial Residents under Artificial Neural Networks in Deep Learning Environment

Special issue on emergence in human-like intelligence toward cyber-physical systems

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