Oniz, YesimKaynak, Okyay2024-07-182024-07-182016978-1-5090-0619-92161-4393https://hdl.handle.net/11411/8706International Joint Conference on Neural Networks (IJCNN) -- JUL 24-29, 2016 -- Vancouver, CANADAIn this paper, a fuzzy spiking neural network structure is developed for the wheel slip regulation problem of an Antilock Braking System. Sliding mode control theory is utilized in the derivation of the update rules for the neural network's weights as well as the parameters of the fuzzy membership functions. Gaussian membership functions are used to convert the sensor readings into the neural networks inputs and the spike response model is employed to denote the effect of the incoming spikes on the postsynaptic membrane potential. The use of the Lyapunov stability method for the derivation of the parameter update rules leads to a stable system response even in the existence of external disturbances.eninfo:eu-repo/semantics/closedAccessConference Object2-s2.0-850071745911036N/A1029N/AWOS:000399925501028