Qing Li, Deling Zheng, Wenbo Meng, and Yong Tang, An Improved Minimum Distance Method Based on Artificial Neural Networks, J. Univ. Sci. Technol. Beijing, 9(2002), No. 1, pp. 74-77.
Cite this article as:
Qing Li, Deling Zheng, Wenbo Meng, and Yong Tang, An Improved Minimum Distance Method Based on Artificial Neural Networks, J. Univ. Sci. Technol. Beijing, 9(2002), No. 1, pp. 74-77.
Qing Li, Deling Zheng, Wenbo Meng, and Yong Tang, An Improved Minimum Distance Method Based on Artificial Neural Networks, J. Univ. Sci. Technol. Beijing, 9(2002), No. 1, pp. 74-77.
Citation:
Qing Li, Deling Zheng, Wenbo Meng, and Yong Tang, An Improved Minimum Distance Method Based on Artificial Neural Networks, J. Univ. Sci. Technol. Beijing, 9(2002), No. 1, pp. 74-77.
MDM (minimum distance method) is a very popular algorithm in state recognition. But it has a presupposition, that is, the distance within one class must be shorter enough than the distance between classes. When this presupposition is not satisfied, the method is no longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstrate that IMDM has two advantages, that is, the rate of recognition is faster and the accuracy of recognition is higher compared with MDM.
MDM (minimum distance method) is a very popular algorithm in state recognition. But it has a presupposition, that is, the distance within one class must be shorter enough than the distance between classes. When this presupposition is not satisfied, the method is no longer valid. In order to overcome the shortcomings of MDM, an improved minimum distance method(IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstrate that IMDM has two advantages, that is, the rate of recognition is faster and the accuracy of recognition is higher compared with MDM.
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