关键词: 极限学习机, PSO-BP, 孔洞修补, 斗齿

Abstract: In view of the large holes on the scattered point cloud model software reverse engineering repair effect is poor and using of the traditional BP neural network algorithm and improved BP feedforward neural network effciency is low,putted forward a algorithm that based on Extreme Learning Machine feedforward neural network repair hole.Taking toy car body point cloud model for example,the artifical hole data is divided into training data and predicted data,adopting ELM to training the traning data,establish regression model and compared with the BP model,PSO-BP model,proved the ELM neural network own the higher rapidity and accurancy.The excavator bucket teeth natural hole as experimental object achieved very good repair effect.There are very good practicability and reference value.

Key words: Extreme Learning Machine(ELM), PSO-BP, hole filling, excavator bucket teeth