Performance characterization of artificial neural networks for contact tracking

Artificial neural networks (ANN's) can be exploited in a variety of information processing applications because they offer simplicity of implementation, possess inherent parallel processing characteristics and are nonlinear and less reliant on modeling of the real process. The paper is concerned with the problem of determining the performance of ANN's trained to provide estimates of contact state variables given a time series of measurements. A method is presented for determining ANN performance. Specifically, performance is shown to be intrinsically related to system observability. A performance analysis of ANN's under various observability conditions is presented along with a methodology for selecting the appropriate ANN-generated solution with a system architecture comprised of multiple clusters of ANN's.<>