Optimized Fuzzy Logic Training of Neural Networks for Autonomous Robotics Applications

Abstract

Abstract — Many different neural network and fuzzy logic related solutions have been proposed for the problem of autonomous vehicle navigation in an unknow n environment. One central problem impacting the success of neural network based solutions is the problem of properly training neural networks. In this paper, an autonomous vehicle controlled by a feed-forward neural network is trained in real time using a fuzzy logic based trainer and the standard back-propagation learning algorithm. The experimental results presented demonstrate the feasibility of real time training using a constrained hardware platform. They also show the impact of racetrack complexity on the training process as well as the impact of the neural network size on the learning speed and error convergence during the training proc ess. The results are then used to develop an optimization procedure that is used to determine the optimal neural netw ork size for the given problem domain and experimental platform.