BP神经网络在图像字符识别中的改进和应用

传统BP神经网络算法虽然具有良好的学习能力和容错能力,但是收敛速度慢,易陷入局部极小点等缺点制约了它的进一步发展和应用.针对这些不足,采用自适应学习率结合附加动量因子的方法可以有效缩短训练时间,加快收敛速度,同时抑制寻优算法陷入局部极小点.将该算法应用于图像字符识别系统中,通过一系列实验优化系统参数之后给出系统识别结果,表明该系统识别具有较高的准确性和鲁棒性.

Improvement and application of BP neural network in image character recognition

The traditional BP neural network algorithm is good in learning ability and fault tolerance,while its disadvantages such as slow convergence rate and easily falling into local minimum restrict its further development and application.An improved BP algorithm with self-adaptive learning rate and additional momentum factors can effectively reduce the training time,speed up the convergence rate and inhibit the possibility of falling into a local minimum.The improved algorithm is applied to the image character recognition system.The influences of model parameters on performance of BP neural network are analyzed,and the recognition results are given after a series of parameter optimization.The experimental results show that the improved BP neural network can recognize image characters with high accuracy and robustness.