Chalak Qazani, Mohamad Reza, Asadi, Houshyar, Al-Ashmori, Mohammed, Mohamed, Shady, Lim, Chee Peng, and Nahavandi, Saeid (2021) Time Series Prediction of Driving Motion Scenarios Using Fuzzy Neural Networks: Motion Signal Prediction Using FNNs. In: Proceedings of the IEEE International Conference on Mechatronics. From: ICM 2021: IEEE International Conference on Mechatronics, 7-9 March 2021, Kashiwa, Japan.

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Abstract

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.

Item ID:	87041
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-7281-4442-9
Copyright Information:	© 2021 IEEE.
Date Deposited:	28 Oct 2025 00:08
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460204 Fuzzy computation @ 30% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 30% 40 ENGINEERING > 4002 Automotive engineering > 400203 Automotive mechatronics and autonomous systems @ 40%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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