Abstract
As the convergence rate of the conventional fuzzy neural network control (FNC) algorithm for a vehicle anti-lock braking system is slow, an improved ant colony optimization fuzzy neural network control (ACO-FNC) algorithm for ABS is proposed, and the control object of ACO-FNC is slip rate. The simulation model of single-wheel ABS is established. According to the comparison of the results of the conventional FNC algorithm and ACO-FNC algorithm, the performance of ACO-FNC algorithm in convergence speed, slip ratio control quality and braking distance is better than FNC algorithm.
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Wang, C., Wang, L. (2014). Research on the Ant Colony Optimization Fuzzy Neural Network Control Algorithm for ABS. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_14
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DOI: https://doi.org/10.1007/978-3-662-45646-0_14
Publisher Name: Springer, Berlin, Heidelberg
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