多层前向神经网络在手写体数字识别应用中的研究

详细信息

The Application of the Feedforword Neural Networks with Multi-layer in the Recognition of Handwritten Numerals

摘要: 针对手写体数字的特点并从实用性的角度出发,采用了一种融字符特征抽取和识别于一体的五层结构的前向传播网络,为了减少连接权值的个数,在网络中采用了权值共享和部分映射,在系统的训练过程中,利用误差函数的二阶导数加速网络的收敛,取得了较好的结果,比传统BP算法快4~5倍。在此基础上,利用二阶导数的信息对网络进行了神经损伤即鲁棒性实验,使网络权值数大大减少。
- 神经网络 /
- 模式识别 /
- 字符识别 /
- 鲁棒性
Abstract: A feedforword network of 5-layer is presented, Which accomplishes numeral features extraction and recognition. This network employed weight share and partial reflection to reduce the total weight numbers. In trainning process, an extension BP algorithm with the second derivative of cost function is used. The algorithm has nice convergence properties, which performs four or five times faster than the conventional BP algorithm. In addition, the network weight numbers are greatly reduced by using the seeond derivative information for neural damage or robustness test.
- neural networks /
- pattern recognition /
- character recognition /
- robustness