Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management
Aiming at the nonlinear time series of automatic building construction management, a neural network prediction model is proposed to analyze and process the nonlinear sequence of deformation monitoring number cutter. The specific content of this method is as follows: for the noise problems existing i...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/7025223 |
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| _version_ | 1849397565205774336 |
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| author | Yunbing Liu |
| author_facet | Yunbing Liu |
| author_sort | Yunbing Liu |
| collection | DOAJ |
| description | Aiming at the nonlinear time series of automatic building construction management, a neural network prediction model is proposed to analyze and process the nonlinear sequence of deformation monitoring number cutter. The specific content of this method is as follows: for the noise problems existing in deformation monitoring data, a wavelet is used to denoise the preprocessing; for the BP network and RBF network commonly used in neural networks, the performance of the two networks is compared and demonstrated by MATLAB program, which proves that RBF neural network can significantly improve the accuracy of deformation prediction. By comparing the results, the maximum relative error of BP network prediction is 18.59%, while the maximum relative error of RBF network prediction is 29.16%, and the average relative error of 13P network prediction is 7.02%, while the average relative error of RBF network prediction value is 10.5%. The comprehensive error of network prediction is 6.1%, RBF network prediction is 8.52%, the standard deviation RMSE of BP network prediction error is 15.347, and that of RBF network prediction error is 21.401, and it shows that the prediction accuracy of BP network is higher than that of RBF network. |
| format | Article |
| id | doaj-art-ad0a968ba1d0408996fe072ca124633c |
| institution | Kabale University |
| issn | 1687-5257 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-ad0a968ba1d0408996fe072ca124633c2025-08-20T03:38:58ZengWileyJournal of Control Science and Engineering1687-52572022-01-01202210.1155/2022/7025223Research on Nonlinear Time Series Processing Method for Automatic Building Construction ManagementYunbing Liu0Architectural Engineering InstituteAiming at the nonlinear time series of automatic building construction management, a neural network prediction model is proposed to analyze and process the nonlinear sequence of deformation monitoring number cutter. The specific content of this method is as follows: for the noise problems existing in deformation monitoring data, a wavelet is used to denoise the preprocessing; for the BP network and RBF network commonly used in neural networks, the performance of the two networks is compared and demonstrated by MATLAB program, which proves that RBF neural network can significantly improve the accuracy of deformation prediction. By comparing the results, the maximum relative error of BP network prediction is 18.59%, while the maximum relative error of RBF network prediction is 29.16%, and the average relative error of 13P network prediction is 7.02%, while the average relative error of RBF network prediction value is 10.5%. The comprehensive error of network prediction is 6.1%, RBF network prediction is 8.52%, the standard deviation RMSE of BP network prediction error is 15.347, and that of RBF network prediction error is 21.401, and it shows that the prediction accuracy of BP network is higher than that of RBF network.http://dx.doi.org/10.1155/2022/7025223 |
| spellingShingle | Yunbing Liu Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management Journal of Control Science and Engineering |
| title | Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management |
| title_full | Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management |
| title_fullStr | Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management |
| title_full_unstemmed | Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management |
| title_short | Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management |
| title_sort | research on nonlinear time series processing method for automatic building construction management |
| url | http://dx.doi.org/10.1155/2022/7025223 |
| work_keys_str_mv | AT yunbingliu researchonnonlineartimeseriesprocessingmethodforautomaticbuildingconstructionmanagement |