Systematic estimation of ANN classification performance employing synthetic data

The use of artificial neural network (ANN) classifiers as a signal processing element in resource constrained embedded computing systems has been restricted due to the difficulty of predicting performance and execution requirements on the deployed platform. In this paper, techniques are presented which provide a means of efficiently estimating data complexity, generating meaningful synthetic data, and evaluating ANN classifiers in terms of achievable performance.