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Proceedings Paper

Neural network for optimization of binary synthetic discrimination functions
Author(s): Ying Liu; Mingzhe Lu; Jianming Zhang; ZhiLiang Fang; Fu-Lai Liu; Guoguang Mu
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Paper Abstract

A Hopfield type neural network was applied to optimize binary correlation synthetic discriminant functions (SDFs). Rotation invariance is achieved while the target object rotates in a certain angle range and a ratio for judgement which is defined as the ratio of the peak value of the average absolute value of a specific point set is given. The optimized binary SDFs (BSDFs) provide the control of the sidelobe levels and the expected shape of the output correlation functions as well as its peak intensity. The simulation results show that when the true target object is presented to the optimized filter, not only the correlation peak is higher than that of the false target objects, but also the order of the magnitude of the ratio for judgement is at least 1 greater than that of the false target objects. The filters perform quite well.

Paper Details

Date Published: 2 March 1994
PDF: 8 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169974
Show Author Affiliations
Ying Liu, Nankai Univ. (China)
Mingzhe Lu, Nankai Univ. (China)
Jianming Zhang, Nankai Univ. (China)
ZhiLiang Fang, Nankai Univ. (China)
Fu-Lai Liu, Nankai Univ. (China)
Guoguang Mu, Nankai Univ. (China)

Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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