Abstract
A multiple-class identification algorithm using genetic neural networks is presented. The algorithm uses a feedforward neural network so it is fast. The algorithm uses the Kohonen network to provide an unsupervised learning. The Kohonen network is used with Z-axis nonrealization. The weight initialization is done by genetic optimization to escape from local minima. The performance of the algorithm is evaluated using a confusion matrix method. The algorithm does not require the number of classes to be known a priori. It also provides a threshold selection method. An example is given to illustrate the application of the algorithm and to evaluate its performance. (Author)