A neural network classifier with disjunctive fuzzy information
This paper presents a neural network classifier that learns disjunctive fuzzy information in the feature space. This neural network consists of two types of nodes in the hidden layer. The prototype nodes and exemplar nodes represent cluster centroids and exceptions in the feature space, respectively. This classifier automatically generates and refines prototypes for distinct clusters in the feature space. The number and sizes of these prototypes are not restricted, so the prototypes will form near-optimal decision regions to meet the distribution of input patterns and classify as many input patterns as possible. Next, exemplars will be created and expanded to learn the patterns that cannot be classified by the prototypes. Such a training strategy can reduce the memory requirement and speed up the process of non-linear classification. In addition, on-line learning is supplied in this classifier and the computational load is lightened. The experimental results manifest that this model can reduce the number of hidden nodes by determining the appropriate number of prototype nodes.
neural network classifier disjunctive fuzzy information prototype exemplar on-line learning trapezoidal fuzzy interval non-linear separability