Optimized Fuzzy Logic Training of Neural Networks for Autonomous Robotics Applications

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Many different neural network and fuzzy logic related solutions have been proposed for the problem of autonomous vehicle navigation in an unknown 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.