A multistrategy signal pattern classifier

Signal patterns are intrinsic to many sensor-based systems. Here we present a multi-strategy signal pattern classifier MSPC that can differentiate among large numbers of signal pattern classes with low classification cost. MSPC combines decision tree concepts, fuzzy rules, genetic algorithms and neural networks to achieve its goal. This combination provides powerful classification capabilities with great tuning flexibility for either performance or cost-efficiency. Fuzzy rules measure similarities between signal patterns. A genetic algorithm finds the best fuzzy classification rule. Neural networks are used in the final classification step and in noise elimination. Machine learning concepts are applied to set the overall algorithm parameters.