EFFECTS OF MEMBERSHIP FUNCTION PARAMETERS ON THE PERFORMANCE OF A FUZZY SIGNAL DETECTOR

This paper describes a signal-detection algorithm based on fuzzy logic . The detector combines evidence provided by two waveform features and explicitly considers uncertainty in the detection decision. The detec tor classifies waveforms as including a signal, not including a signal , or being uncertain, in which case no conclusion regarding presence o r absence of a signal is drawn, Piecewise linear membership functions are used, and a method to describe the membership functions in terms o f two parameters is developed, The performance of the detector is comp ared to a Bayesian maximum likelihood detector, using brainstem audito ry evoked potential signals in simulated noise, and the effects of the steepness (slope) and overlap of the membership functions on detector performance are evaluated, By varying the membership function steepne ss and overlap, the fuzzy detector can almost completely eliminate cla ssification errors at the cost of a large number of uncertain classifi cations or it can be made to perform similarly to the Bayesian detecto r.