Bridging the fuzzy, neural and evolutionary paradigms for automatic target recognition
Multilayer perceptron neural networks possess pattern recognition properties that make them well suited for use in automatic target recognition systems. Their application is hindered, however, by the lack of a training algorithm, which reliably finds a nearly global optimal set of weights in a relatively short time. The approach presented here is based on implementation of genetic algorithms and fuzzy logic in training the proposed hybrid architecture. Compared to other approaches, it offers the following three main advantages. The neuro-computing technique is capable of fast and adaptive distortion-invariant pattern recognition for rapidly changing targets. On the other hand, genetic algorithms and fuzzy logic offer very sophisticated configuration control, which combines the results of previous computations with the external operating environment. Third, it allows us to significantly improve the reliability of object detection in the input scene with respect to associated distortions at no additional computational cost. This paper examines using genetic algorithms as an efficient way to train a feedforward neural net, the inputs for which are provided by a fuzzy front end, to be applied to automatic target recognition. The system is tested using actual laser detection and range data as training data and the results of the analysis show that the proposed system results in a much faster convergence on a weight set, and a high rate of successful recognition.
automatic target recognition genetic algorithm fuzzy logic feedforward neural networks