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Weighted error-output recurrent echo kernel state network for multi-step water level prediction. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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A novel feature selection method via mining Markov blanket. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03863-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Mine Microseismic Time Series Data Integrated Classification Based on Improved Wavelet Decomposition and ELM. Cognit Comput 2022. [DOI: 10.1007/s12559-022-09997-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Liu A, Zhao D, Li T. A data classification method based on particle swarm optimisation and kernel function extreme learning machine. ENTERP INF SYST-UK 2021. [DOI: 10.1080/17517575.2021.1913764] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Ao Liu
- Naval Aviation University, Yantai, Shandong, China
| | - Dongning Zhao
- Shenzhen Vetose Technology Co.Ltd, Shenzhen, Guangdong, China
| | - Tingjun Li
- Naval Aviation University, Yantai, Shandong, China
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Analysis of Driver Performance Using Hybrid of Weighted Ensemble Learning Technique and Evolutionary Algorithms. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-020-05115-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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Comprehensive Evaluation of Power Quality Based on an Improved TOPSIS Method Considering the Correlation between Indices. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9173603] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In order to improve the scientific and rationality of power quality (PQ) comprehensive evaluation, an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) strategy in consideration of the correlation between indices is proposed to evaluate PQ. The strategy overcomes the shortcomings of the traditional methods that ignore the correlation between PQ performance parameters. Firstly, the AHP-entropy weight (EW) method is obtained by combining the improved analytic hierarchy process (AHP) and the EW method, and the combined weights of the PQ indices are calculated. Secondly, the Mahalanobis distance is used to replace the Euclidean distance in the traditional TOPSIS method, and the PQ samples that need to be evaluated are sorted. The Mahalanobis distance nonlinearly correlates the components inside the evaluation matrix through its own covariance matrix, which solves the problem that the dimensions of each index are not uniform and eliminates the correlation interference between the indices. The example shows that the improved TOPSIS method effectively avoids the misjudgment caused by the correlation between the indices, and the evaluation results are more reasonable and scientific, with greater superiority and effectiveness.
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