A new sampling method in particle filter based on Pearson correlation coefficient

Zhou, Haijun; Deng, Zhihong; Xia, Yuanqing; Fu, Mengyin

doi:10.1016/j.neucom.2016.07.036

Cited by 288 publications

(131 citation statements)

References 30 publications

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“…The Pearson's correlation coefficient reflects the degree of linear correlation between the two variables and is one of the most widely used relational measures. It is defined as the product of the covariance of two variables divided by the product of the standard deviation of the two variables [20]; Pearson's correlation coefficient is expressed as shown in Equation (1):…”

Section: Feature Band Selectionmentioning

confidence: 99%

Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing

Zhao

Zhou

et al. 2018

Sustainability

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Mercury is one of the five most toxic heavy metals to the human body. In order to select a high-precision method for predicting the mercury content in soil using hyperspectral techniques, 75 soil samples were collected in Guangdong Province to obtain the soil mercury content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A multiple linear regression (MLR), a back-propagation neural network (BPNN), and a genetic algorithm optimization of the BPNN (GA-BPNN) were used to establish a relationship between the hyperspectral data and the soil mercury content and to predict the soil mercury content. In addition, the feasibility and modeling effects of the three modeling methods were compared and discussed. The results show that the GA-BPNN provided the best soil mercury prediction model. The modeling R 2 is 0.842, the root mean square error (RMSE) is 0.052, and the mean absolute error (MAE) is 0.037; the testing R 2 is 0.923, the RMSE is 0.042, and the MAE is 0.033. Thus, the GA-BPNN method is the optimum method to predict soil mercury content and the results provide a scientific basis and technical support for the hyperspectral inversion of the soil mercury content.

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Section: Feature Band Selectionmentioning

confidence: 99%

Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing

Zhao

Zhou

et al. 2018

Sustainability

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“…Through the calculation, it can be obtained that the Spearman correlation coefficients [36] of the two are 0.6153 and 0.1239 in the periods of stable fluctuation and sharp volatility. At the same time, based on the Pearson correlation coefficient formula [37,38], we find that the correlation values of them in stable fluctuation period and violent fluctuation period are 0.9013 and 0.1410, respectively. However, it is acknowledged that the Pearson correlation coefficient is more accurate and more sensitive than the Spearman rank correlation coefficient, and only if the Pearson correlation coefficient does not significantly deviate from the Spearman rank correlation coefficient can we use the former, if the difference between the two is too large, then the original data is irregular.…”

Section: The Correlation Between Hspfn and Hfpfnmentioning

confidence: 84%

Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks

Chen

Wang

et al. 2017

Sustainability

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“…Historically, it is the first formal correlation measure and it is still one of the most widely-used relation measures [45]. The Pearson correlation coefficient of two variables X (socioeconomic forces, such as UR, PU, GDP, STI, etc.)…”

Section: Pearson Correlation Coefficientmentioning

confidence: 99%

Spatial–Temporal Patterns and Driving Forces of Ecological-Living-Production Land in Hubei Province, Central China

et al. 2017

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Ecological-living-production land (ELPL) is gaining an increasing attention of governors, planners and scholars to alleviate ecological deterioration on the premise of ensuring regional sustainable development in China. This paper has built an ecological-living-production land classification system (ELCS) by the reclassification method, and further analyzed the spatio-temporal characteristics and evolution mechanism of ELPL from 2009 to 2014 with a case study of Hubei Province of Central China. The results show that (1) land with an ecological function held a dominant role in Hubei Province. Ecological land (EL) and production-eco land (PEL) covered the largest areas. The area of EL was the largest in Western Hubei Eco-cultural Tourism Circle (WHETC), and the area of PEL accounted for the largest proportion in Wuhan Urban Circle (WUC). (2) Land with an ecological function was decreasing continuously, while the land with living function expanded rapidly. Additionally, the intensity of ELPL changes in the WUC was higher than that in the WHETC. (3) The changes of ELPL threatened the food and ecological security and adversely affected the sustainable development. The factors of population growth and GDP increase were the main driving forces of ELPL change. The results of this study provide valuable information for planning decision makings (e.g., the ELPL spatial pattern optimization).

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A new sampling method in particle filter based on Pearson correlation coefficient

Cited by 288 publications

References 30 publications

Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing

Estimation Methods for Soil Mercury Content Using Hyperspectral Remote Sensing

Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks

Spatial–Temporal Patterns and Driving Forces of Ecological-Living-Production Land in Hubei Province, Central China

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