基于组合神经网络的农用车轮胎号识别方法

为了保证轮胎号识别系统具有较高的识别正确率和置信度，对轮胎号字符识别方法进行了研究。对大量的农用车轮胎号字符图像样本进行各类特征量提取，针对每类特征量建立各自的子BP网络进行训练，并将各训练好的子网络进行组合形成并行组合神经网络，按照等权平均或投票选举决策得出最终识别结果。并行组合神经网络的连接数较传统庞大单级神经网络少，训练和识别的速度要快，大量的轮胎号字符样本识别试验表明，并行组合神经网络的识别正确率和置信度都较传统BP网络得到提高。

Farm vehicle tire code recognition based on combination neural network

In order to ensure that the recognition system of tire code has preferable recognition correct rate and confidence level, the tire code recognition method was studied. In this method, various kinds of characteristics were extracted among a great deal of farm vehicle tire code character images. Regarding every kind of characteristics as the BP neural network input value, an individual BP neural network was set up and trained. After combining these sub-BP neural networks into a combination neural network, the recognition results were obtained conforming to the averaging or voting rule. The connection numbers of assembled neural network are far less than that of traditional huge neural network, and its training and recognition speed is also faster than that of traditional neural network. A lot of experiments of the tire code sample recognition show that the recognition correct rate and confidence level of assembled neural network are higher than those of the traditional neural network.