CAJ | 학술논문

　　基于Hu不变矩的尺度不变性，以图像的不变矩特征作为输入，建立基于批训练的改进型误差反向传播(BP)神经网络。运用基于Bayesian正则化的Levenberg-Marquardt算法优化误差函数计算精度，改进网络，实现参数最优化组合。通过MAT?LAB环境，建立了基于不变矩的改进BP神经网络目标识别模型。实验表明，该方法实现了对目标的准确识别和对干扰图像的正确判断。
　　기우Hu불변구적척도불변성，이도상적불변구특정작위수입，건립기우비훈련적개진형오차반향전파(BP)신경망락。운용기우Bayesian정칙화적Levenberg-Marquardt산법우화오차함수계산정도，개진망락，실현삼수최우화조합。통과MAT?LAB배경，건립료기우불변구적개진BP신경망락목표식별모형。실험표명，해방법실현료대목표적준학식별화대간우도상적정학판단。
Based on the geometrical invariability of Hu invariant moment, and taking the characteristics of image invariant moment as an input, an improved error back propagation (BP) neural network for batch training is established. According to Bayesian normalization Levenberg-Marquardt algorithm, the calculation accuracy of error functions is optimized and the network is improved so as to realize the optimized parameter combination. An improved BP neural network object recognition model based on invariant moment is built in MATLAB envi?ronment. Experimental results show that accurate target recognition and correct interferential image estimation are implemented with the method.