A Numerical Method of Statistical Pattern Recognition

Xu Hong; Li Hongji; Wang Ronghua

doi:10.1007/BF02659082

Impact Factor: 5.8

Volume 6 Issue 4

Oct. 1989

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 1989 > 6(4): 483-492

A Numerical Method of Statistical Pattern Recognition

1.
Air Force Meteorological Research Institute, Beijing, 100085,Air Force Meteorological Research Institute, Beijing, 100085,Air Force Meteorological Research Institute, Beijing, 100085

doi: 10.1007/BF02659082

Abstract
References
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Abstract:
A numerical method of statistical pattern recognition is proposed in this paper. Different from the discriminatory analysis method currently used in the mathematic statistics, it is unnecessary to assume that the predictand should be subject to a certain distribution. On the contrary, the statistical relationship between predictand and predictor has been obtained directly with computer according to actual distribution to recognize the category of patterns. Result of forecast has been improved as compared with the usual analytic discriminatory method. The influence of predictor on predictand can be seen clearly from this method and the transparency is good. Therefore, it is better to use the method in very short range forecast for which causality is more obvious.
References

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Manuscript History

Manuscript received: 10 October 1989

Manuscript revised: 10 October 1989

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

A Numerical Method of Statistical Pattern Recognition

1. Air Force Meteorological Research Institute, Beijing, 100085,Air Force Meteorological Research Institute, Beijing, 100085,Air Force Meteorological Research Institute, Beijing, 100085

Manuscript received: 1989-10-10
Manuscript revised: 1989-10-10

Abstract: A numerical method of statistical pattern recognition is proposed in this paper. Different from the discriminatory analysis method currently used in the mathematic statistics, it is unnecessary to assume that the predictand should be subject to a certain distribution. On the contrary, the statistical relationship between predictand and predictor has been obtained directly with computer according to actual distribution to recognize the category of patterns. Result of forecast has been improved as compared with the usual analytic discriminatory method. The influence of predictor on predictand can be seen clearly from this method and the transparency is good. Therefore, it is better to use the method in very short range forecast for which causality is more obvious.