An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures

Abstract

Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the robot field. For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. The simulation results with the robot execution failures datasets show that, by optimizing the kernel parameters, the proposed algorithm has good generalization performance and outperforms KELM and the other approaches in terms of classification accuracy. Other benchmark problems simulation results also show the efficiency and effectiveness of the proposed algorithm.