Identification of Temperature Drift for FOG Using RBF Neural Networks

Temperature drift is a nonnegligible factor causing big biasing error in FOG. How to identify and compensate this error relates directly to the measurement accuracy. After comparing the features of BP and RBF networks, the latter was applied to identify the temperature drift as it can achieve the global optimum evaluation and has the linear weight combiner and fast learning. Of the different approaches of parameter learning, the OLS algorithm is prior by its simplicity, high accuracy and fast speed. The simulation results show that the RBF network based method with the OLS learning offers a powerful and successful procedure for fitting and compensating the temperature drift.