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.

基本的模糊神经网络算法收敛速度慢，计算量大，为此设计了一种粒子群优化训练的模糊神经网络算法。传统的粒子群优化算法容易陷入局部极限，效率低下，针对惯性权重和学习因素设计了新的更新规则来克服这些问题。我们还设计了用于改进的粒子群算法的训练规则，以训练模糊神经网络，并采用混合算法解决了智能驾驶汽车的路径规划问题。实验证明了该算法的有效性和实用性。