A neural architecture for fuzzy classification with application to complex system tracking

The application of a new architecture for fuzzy pattern classification is described to address the problem of tracking complex, discrete event driven systems. The classifier relies upon the integration of fuzzy logic techniques with an artificial neural architecture to produce an efficient mechanism for classifying input patterns. In addition, the fuzzy classifier provides a method for quantifying and handling ambiguity near the decision surfaces. The proposed application consists of classifying input feature patterns as events which drive complex, dynamic systems modeled as discrete event systems