二元综合鉴别函数的神经网络优化

Neural Network for Optimization of Binary Synthetic Discrimination Functions

根据Ｈｏｐｆｉｅｌｄ神经网络的优化功能，对综合鉴别函数进行二元优化，使相关输出具有期望的形状及峰值大小，从而实现旋转不变识别，并定义了一个判别依据——判别比．计算机模拟的结果表明，目标物体通过优化的二元滤波器后，不仅具有期望输出，而且判别比要比伪目标物体至少大一个量级．

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 to 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 result shows that when the target object is presented to the optimized filter, not only the correlation peak is as high as expected and higher than that of the nontarget objects, but also the order of the magnitude of the ratio for judgement is at least 1 greater than that of the non-target objects. The recognition ability of the filter is very Strong.