An electronic nose and modular radial basis function network classifiers for recognizing multiple fragrant materials

An electronic nose with 16 TGS sensors is developed for effectively recognizing numerous kinds of odors. In order to solve large-sample, multi-class and high-dimensional classification problems, this paper proposes a type of modular radial basis function (RBF) network classifiers, in which every module consists of a single-layer RBF network and a single-layer perceptron. The method for optimally determining the number, locations and widths of RBF kernels and the target values of Gaussian activation functions is gone into details. The presented adaptive algorithm, which only propagates error one layer backwards, has much lower computational complexity than the back-propagation algorithm used in multilayer perceptrons. The electronic nose with the modular adaptive RBF neural network classifiers is able to reach the recognition rate of 96.67% for 21 kinds of simple and complex fragrant materials. The experimental result for the extend training set, which consists of 4050 samples and 84 classes, shows that the modular RBF networks as well as the adaptively learning algorithm have faster convergence rate, higher classification accuracy, larger probability to get optimal structures, and better ability to reach global minimum points, compared with the standard RBF networks and multilayer perceptrons. Therefore, the presented modular RBF networks are quite suitable for large sample problems. (C) 2003 Elsevier B.V. All rights reserved.