In view of the low accuracy of the current digital image recognition and classification methods for early education ro-bots,which leads to the unsatisfactory accuracy of the robot arm digital writing of early education robots,an intelligent model is pro-posed to improve it.Firstly,digital image preprocessing is implemented based on a Whale optimization algorithm(WOA)improved Pulse Coupled Neural Network(PCNN);Then combine WOA and Seagull Optimization Algorithm(SOA)to improve the perform-ance of SOA;Finally,the improved SOA is used to improve the BP neural network(BPNN).Combining the above content,a digital image recognition and classification model based on improved BPNN is constructed to improve the accuracy of the robot's robotic arm in writing digits.The results show that after applying this model,the digital writing accuracy of the early education robot is 98.61％,which is 7.53％～10.18％higher than other models.Therefore,the method proposed in the study can effectively improve the accura-cy of the robotic arm for early childhood education to write numbers,providing a new path for children's early childhood education.
关键词
早教机器人/机械臂/BPNN/数字识别/海鸥优化算法
Key words
early education robot/mechanical arm/BPNN/digital identification/seagull optimization algorithm
维普
万方数据