A novel neural network based technique instead of now available measures to raise FOG's measurement precision was proposed in the paper. The technique includes a few of aspects: a series-single-layer neural network used for dealing with the random noises, an advanced learning scheme corresponding with the above network, and an RBF network based method to evaluate the temperature drift. The series- single-layer network, which is composed of two single-layer networks in series, has advantages of simple architecture, fast learning speed, and better performance over conventional BP networks. To conduct the learning of the series-single-layer network, the advanced learning scheme derived from drawing the increments of error into energy function was then developed after referring to the unstable power law noise. Furthermore, in consideration of their good performances, we introduced an RBF network to evaluate and compensate the temperature drift and selected OLS algorithm to construct the network with high speed. The simulation results verified the validity that the proposed technique greatly decreased the errors induced by the different random noises and the biasing drift existing in FOG system.

Date Published: 24 March 2000
PDF: 8 pages
Proc. SPIE 3936, Integrated Optics Devices IV, (24 March 2000); doi: 10.1117/12.379962

Published in SPIE Proceedings Vol. 3936:
Integrated Optics Devices IV
Giancarlo C. Righini; Seppo Honkanen, Editor(s)