System identification of tracking error and evaluation of tracking performance using BP neural network
Ning Zhang, Shen Xiang-heng · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
A novel approach for evaluating the tracking performance of optoelectronic theodolite is proposed. First, an equivalent mathematic model of tracking error is established. Then, the equivalent sine signal is inputted to the equivalent model, and the outputs are sampled. The results of evaluating the tracking performance are obtained based on the statistical calculation of output produced by equivalent model. Equivalent model using the BP (Backprogration) neural network structure is identified. The training method of BP neural network adopts the LM (Levenberg-Marquardt) algorithm for the sake of speeding up training process. The BP neural network is trained and tested by using the training and testing samples gotten from the simulation model of optoelectronic theodolite tracking system under MATLAB/SIMULINK. The estimate errors of equivalent model including average error, maximum error and standard error are 2.5872e-006°≈0°, 2.8" and 1.9". The results show that the equivalent model identified based on BP neural network meets the needs of evaluating the tracking performance of optoelectronic theodolite. The accurate evaluation of tracking performance is achieved.