Time Series Prediction of Driving Motion Scenarios Using Fuzzy Neural Networks: Motion Signal Prediction Using FNNs

Motion signals can be reproduced using a simulation-based motion platform (SBMP) and virtual reality. In this respect, time series prediction of the driving motion scenarios can enhance the quality of the regenerated motion signals with the motion cueing algorithm (MCA). Specifically, the MCA is employed to regenerate the motion signals for a SBMP with respect to the workspace limitations. The use of the feedforward neural network (NN) produces inaccurate predictions pertaining to the driving motion scenarios. In this paper, an interval type-2 fuzzy neural network (FNN) is proposed to predict the driving motion scenarios. As type-1 FNN is not able to represent the uncertainty in information, a Type-2 Quantum (T2Q) FNN is used to handle the undefined indexes with consideration of uncertain jump positions. The T2QFNN model can identify the overlaps between classes and adjust the fuzzy parameters automatically, including fuzzy rules as a linear combination of the exogenous input variables. The simulation results indicate that T2QFNN is able to yield lower prediction error and shorter learning time as compared with those from the feedforward NN and type-1 FNN models.