Target trajectory estimation by unambiguous phase differences from a single fixed passive sensor

Ren, Jinlong; Wei, Guohua; Bai, Jian‐Fei; Bao, Xiaojie; Wang, Xu

doi:10.1049/el.2020.1660

Cited by 1 publication

(1 citation statement)

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“…Shen et al determined the driving effect of shared information on supply chain management by comprehensively analyzing the problem of forecasting information sharing in supply chain management so that it can better match supply chain demand [17]. Li et al built an effective forecasting model to reasonably control the multi-item inventory in the supply chain [18]. Chaudhuri et al integrated extreme learning machines to propose an optimized forecasting model, thereby realizing real-time accurate forecasting of products in supply chain management [19].…”

Section: Related Workmentioning

confidence: 99%

Research on the Application of Multi-Objective Algorithm Based on Tag Eigenvalues in e-Commerce Supply Chain Forecasting

Huang,

Lian

2023

IJACSA

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With the continuous development of Internet technology, the scale of Internet data is increasing day by day, and business forecasting has become more and more important in corporate business decision-making. Therefore, to improve the accuracy of Multi Target Regression in the actual e-commerce supply chain forecasting, research through the method of constructing the labeling feature for each target is optimized, the Multi-Target Regression via Sparse Integration and Label-Specific Features algorithm is obtained, and the experimental analysis is carried out on the performance of the algorithm and the application effect in the actual e-commerce supply chain. The experimental results show that the average of Relative Root Mean Square Error value of the research algorithm and is the lowest in most datasets, with a minimum of 0.058 in the effect experiments of prediction and label-specific features; in the effect and flexibility experiments of sparse sets, the lowest average of Relative Root Mean Square Error value of the research algorithm was 0.058, and the average rank value was the smallest. In addition, the average of Relative Root Mean Square Error value of the research algorithm is the smallest under the target variable of Y2 in the Enb data, and its value is 0.075. In the actual e-commerce supply chain forecast, the research algorithm has the highest score of 0.097 points. Overall, research algorithm has a better forecasting effect and higher performance, and has better practicality in practice, and can play a better effect in actual e-commerce supply chain forecasting.

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Section: Related Workmentioning

confidence: 99%