A path planning method based on the particle swarm optimization trained fuzzy neural network algorithm

• Published in: Cluster computing Vol. 24; no. 3; pp. 1901 - 1915
• Main Authors: Liu, Xiao-huan, Zhang, Degan, Zhang, Jie, Zhang, Ting, Zhu, Haoli
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Basic converters; Computer Communication Networks; Computer Science; Design; Fuzzy logic; Genetic algorithms; Neural networks; Operating Systems; Optimization; Particle swarm optimization; Path planning; Planning; Processor Architectures; Path planning; Particle swarm algorithm; Fuzzy neural network; Intelligent driving
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-021-03235-1

The basic fuzzy neural network algorithm has slow convergence and large amount of calculation, so this paper designed a particle swarm optimization trained fuzzy neural network algorithm to solve this problem. Traditional particle swarm optimization is easy to fall into local extremes and has low efficiency, this paper designed new update rules for inertia weight and learning factors to overcome these problems. We also designed training rules for the improved particle swarm optimization to train fuzzy neural network, and the hybrid algorithm is applied to solve the path planning problem of intelligent driving vehicles. The efficiency and practicability of the algorithm are proved by experiments.