In the classification learning tasks, it is inevitable to generate noise in the process of acquiring data. Especially, the existence of label noise not only makes the learning model more complex, but also leads to overfitting and the reduction of generalization ability of the classifier. Although some label noise filtering algorithms can solve the above problems to some extent, there are still some limitations such as poor noise recognition ability, unsatisfactory classification effect and low filtering efficiency. Focused on these issues, a local probability sampling method based on label confidence distribution was proposed for label noise filtering. Firstly, the random forest classifiers were used to perform the voting of the labels of samples, so as to obtain the label confidence of each sample. And then the samples were divided into easy and hard to recognize ones according to the values of label confidences. Finally, the samples were filtered by different filtering strategies respectively. Experimental results show that in the situation of existing label noise, the proposed method can maintain high noise recognition ability in most cases, and has obvious advantage on classification generalization performance.

Aiming at the problem that the joint velocity of the rail manipulator deviates from the expected value during the process of task execution, a pseudoinverse-based motion planning scheme for deviation correction of joint velocity of rail manipulator was proposed. Firstly, according to the joint angle state of the manipulator and the motion state of the end-effector, the pseudoinverse algorithm was used to analyze the redundancy of the rail manipulator on the velocity level. Secondly, a time-varying function was designed to perform constraint and adjustment of the joint velocity, making the deviated joint velocity converge to the expected value. Thirdly, an error correction method was employed to reduce the position error of the end-effector for ensuring the successful execution of the trajectory tracking task. Finally, the motion planning scheme was simulated on Matlab software with the four-bar redundant manipulator with the base of linear movement and circular movement as the example. The simulation results show that the proposed motion planning scheme can correct the joint velocity of the rail manipulator deviated from the expected value during the task execution, and can make the end-effector obtain higher accuracy in trajectory tracking.