Modular Weightless Neural Network Architecture for Intelligent Navigation

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Executive Summary

The standard Multi Layer Perceptron Neural Network (MLPNN) type has various drawbacks, one of which is training requires repeated presentation of training data, which often results in very long learning time. An alternative type of network, almost unique, is the Weightless Neural Network (WNNs) this is also called n-tuple networks or RAM based networks. In contrast to the weighted neural models, there are several one-shot learning algorithms for WNNs where training takes only one epoch. This paper describes WNNs for recognizes and classifies the environment in mobile robot using a simple microprocessor system. The authors use a look-up table to minimize the execution time, and that output stored into the robot RAM memory and becomes the current controller that drives the robot.

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