BP神经网络字符识别系统Matlab建模及硬件实现

MATLAB Modeling and Hardware Implementation of Character Recognition System Base on BP Neural Network

为了设计基于BP神经网络的字符识别系统模型并训练其参数,能够以较低硬件代价工程实现该系统,作者利用Matlab建立了一个基于BP神经网络的字符识别系统模型,对5000个样本进行了标记,并利用这些样本对模型进行了训练和验证,识别率达到了85.20%.同时,利用FPGA及Verilog硬件描述语言设计了该系统的神经元硬件电路,效果与Matlab的仿真数据一致.利用Altera公司的FPGA芯片实际综合下载了神经元及相关系统,能够实现对字符图像文件数据的计算.

The authors used Matlab to set up a character recognition system model based on BP neural network. As a result, its parameters can be trained and the system can be implemented at a low engineering hardware cost. We tagged 5000 samples which are used for model training and validation. The recognition rate of the method we propose reaches 85.20%. Meanwhile we use FPGA and Verilog HDL to design the neuron hardware circuit of the system, and the effect is consistent with the Matlab simulation data. The neuron circuit and related system is downloaded to a FPGA chip of Altera so that the data of character image file can be calculated.