A general closed-loop framework for multi-dimensional sequence processing

Abstract

A novel closed-loop framework for multidimensional sequence processing is proposed in this paper. Traditional sequence-driven models are always forward, so no information is feedback to correct their outputs, which may deviate from the true values gradually due to the estimation error accumulating. To overcome the problem, the multidimensional vector in the input sequence is divided into two vectors based on its data attribute. One vector sequence generated from the original input sequence is considered as the new input sequence, and the other is considered as the measurement output sequence. The original output sequence is treated as the state sequence. Then, a closed-loop model in the state-space form is constructed, with which the states can be estimated online by filtering algorithms. The feasibility of the proposed framework has been verified by using the robot inverse kinematics. © 2013 Springer-Verlag.