Abstract
Neural networks (NN) have made a great impact in modeling and synthesizing non-linear mapping of input-output space. In this paper, we describe the design and testing of a particular class of NN, radial basis function networks, for dryness prediction in a clothes dryer. Our objective is to design an improved, robust, accurate, and adaptive system for dryness prediction, leading to a low-cost, energy-efficient, electronically controlled clothes dryer. We synthesize a stepwise radial basis function network to predict clothes moisture content and optimize the number of required sensors, while providing learning capabilities to account for external disturbances. In addition, we quantize clothes moisture content into “degrees of dryness”, thus enhancing the prediction accuracy. We show that this approach results in a predictor that is superior to non-linear regression and multi-layer perceptron tools.