Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things
Time series have broad usage in the wireless Internet of Things. This article proposes a nonlinear time series prediction algorithm based on the Small-World Scale-Free Network after the AIC-Optimized Subtractive Clustering Algorithm (AIC-DSCA-SSNET, AD-SSNET) to predict the nonlinear and unstable ti...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211004112 |
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| _version_ | 1832556857308217344 |
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| author | Banteng Liu Wei Chen Meng Han Zhangquan Wang Ping Sun Xiaowen Lv Jiaming Xu Zegao Yin |
| author_facet | Banteng Liu Wei Chen Meng Han Zhangquan Wang Ping Sun Xiaowen Lv Jiaming Xu Zegao Yin |
| author_sort | Banteng Liu |
| collection | DOAJ |
| description | Time series have broad usage in the wireless Internet of Things. This article proposes a nonlinear time series prediction algorithm based on the Small-World Scale-Free Network after the AIC-Optimized Subtractive Clustering Algorithm (AIC-DSCA-SSNET, AD-SSNET) to predict the nonlinear and unstable time series, which improves the prediction accuracy. The AD-SSNET is introduced as a reservoir based on the echo state network to improve the predictive capability of nonlinear time series, and combined with artificial intelligence method to construct the prediction model training samples. First, the optimal clustering scheme of randomly distributed neurons in the network is adaptively obtained by the AIC-DSCA, then the AD-SSNET is constructed according to the intra-cluster priority connection algorithm. Finally, the reservoir synaptic matrix is calculated according to the synaptic information. Experimental results show that the proposed nonlinear time series prediction algorithm extends the feasible range of spectral radii of the reservoir, improves the prediction accuracy of nonlinear time series, and has great significance to time series analysis in the era of wireless Internet of Things. |
| format | Article |
| id | doaj-art-ed2b27af53fb44cca88c31ed7ccc2431 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ed2b27af53fb44cca88c31ed7ccc24312025-02-03T05:44:18ZengWileyInternational Journal of Distributed Sensor Networks1550-14772021-03-011710.1177/15501477211004112Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of ThingsBanteng Liu0Wei Chen1Meng Han2Zhangquan Wang3Ping Sun4Xiaowen Lv5Jiaming Xu6Zegao Yin7College of Engineering, Ocean University of China, Qingdao, ChinaSchool of Information Science & Engineering, Changzhou University, Changzhou, ChinaData-driven Intelligence Research (DIR) Lab, College of Computing and Software Engineering, Kennesaw State University, Marietta, GA, USACollege of Information Science and Technology, Zhejiang Shuren University, Hangzhou, ChinaCollege of Information Science and Technology, Zhejiang Shuren University, Hangzhou, ChinaCollege of Information Science and Technology, Zhejiang Shuren University, Hangzhou, ChinaCollege of Science, Virginia Tech (Virginia Polytechnic Institute and State University), Blacksburgh, VA, USACollege of Engineering, Ocean University of China, Qingdao, ChinaTime series have broad usage in the wireless Internet of Things. This article proposes a nonlinear time series prediction algorithm based on the Small-World Scale-Free Network after the AIC-Optimized Subtractive Clustering Algorithm (AIC-DSCA-SSNET, AD-SSNET) to predict the nonlinear and unstable time series, which improves the prediction accuracy. The AD-SSNET is introduced as a reservoir based on the echo state network to improve the predictive capability of nonlinear time series, and combined with artificial intelligence method to construct the prediction model training samples. First, the optimal clustering scheme of randomly distributed neurons in the network is adaptively obtained by the AIC-DSCA, then the AD-SSNET is constructed according to the intra-cluster priority connection algorithm. Finally, the reservoir synaptic matrix is calculated according to the synaptic information. Experimental results show that the proposed nonlinear time series prediction algorithm extends the feasible range of spectral radii of the reservoir, improves the prediction accuracy of nonlinear time series, and has great significance to time series analysis in the era of wireless Internet of Things.https://doi.org/10.1177/15501477211004112 |
| spellingShingle | Banteng Liu Wei Chen Meng Han Zhangquan Wang Ping Sun Xiaowen Lv Jiaming Xu Zegao Yin Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things International Journal of Distributed Sensor Networks |
| title | Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things |
| title_full | Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things |
| title_fullStr | Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things |
| title_full_unstemmed | Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things |
| title_short | Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things |
| title_sort | nonlinear time series prediction algorithm based on ad ssnet for artificial intelligence powered internet of things |
| url | https://doi.org/10.1177/15501477211004112 |
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