Acceleration signal estimation using neural networks
Xiao‐Zhi Gao, S.J. Ovaska · Measurement Science and Technology · 2001
In this paper, we propose a neural-network-based approach to acquiring angular acceleration from a noisy velocity signal. Our scheme consists of two cascaded neural networks: neural network I (NN I) and neural network II (NN II). NN I attenuates harmful measurement noise from the velocity input. NN II further reduces the residual noise level, and gives the one-step-ahead prediction of the final acceleration signal. As an illustrative example, we discuss the application of our method in the elevator velocity and acceleration acquisition problem. Two different kinds of neural network model are employed here, the back-propagation neural network (BP) and the adaptive-network-based fuzzy inference system (ANFIS), to act as NN I and NN II. We also compare the performances of these two neural networks using numerical simulations.