In order to improve the measurement accuracy of three-dimensional (3D) scanning robot, a method of 3D scanning robot system error modeling and compensation based on particle swarm optimization radial basis function neural network (PSO-RBFNN) is proposed to achieve intelligent compensation of measurement error. The structure, calibration and error modeling process of 3D scanning robot system are mainly described. Cleverly using the iterative closest point (ICP) algorithm to construct input and output data pairs of neural network, and the specific process of error modeling using PSO-RBFNN is given. Finally, through the actual experiment we test and verify the correctness and effectiveness of the proposed error modeling and compensation method by measuring the distance between the centers of two standard balls. Experimental results show: the proposed error model and the compensation method can effectively compensate the measurement errors and improve the accuracy of the 3D scanning robot system.

With the advancement of science and technology development and the development of social , more and more current information technology and computer technology has been used in the various aspects of life and work, the current rapid development of network technology has penetrated into all aspects of life. With the development of network technology and the text messaging of network gradually strengthened, wide and messy network information, in the vast network of information, how to efficiently access the information that we need quickly, which is related to the text mining technologies. The deep learning is a new learning method of the machine learning, it simulates the human brain and analysis the neural network through the imitation of the human brain and the interpretation of the relevant data. In the text mining, the application of the deep learning can be a very good text clustering and text classification, it is easy to find the desired text information, so the application of the deep learning plays an important role in the deep learning. Research for this paper analyzes the deep learning in text mining and the related content of knowledge.