基于模糊神经网络系统的智能小车避障

智能小车把超声波传感器和红外传感器相结合来感知外界环境的信息,并按照一定的规则来调整小车的方位角和速度,实现智能小车的自主导航和避障。模糊神经网络作为人工智能的分支，兼具模糊逻辑系统和神经网络各自的优点，具有表达和处理确定的信息、模糊信息的能力和良好的学习能力等特点。把模糊逻辑系统和神经网络结合起来，运用到智能小车避障的自适应控制中，并且使用一种多层前馈型神经网络即BP神经网络在模糊神经系统中解决神经网络的权系数优化问题。

Smart Car Obstacle Avoidance Based on Fuzzy Neural Network System

Smart cars combine the ultrasonic sensors and infrared sensors to perceive the environment information, and according to certain rules to adjust the azimuth Angle and speed of the car, to realize the autonomous navigation of intelligent car. Fuzzy neural networks as a branch of artificial intelligence have the advantages of fuzzy logic system and neural network respectively, which have the ability to deal with certain information, fuzzy information and good learning ability, etc. Combining fuzzy logic system and neural network is applied to intelligent adaptive control of the car obstacle avoidance, type and the use of a multilayer feed forward neural network BP neural network in the fuzzy neural system to solve the optimization problems of weights of neural network.