RBF-based neurodynamic nearest neighbor classification in real pattern space

Superposition of radial basis functions centered at given prototype patterns constitutes one of the most suitable energy forms for gradient systems that perform nearest neighbor classification with real-valued static prototypes. It is shown in this paper that a continuous-time dynamical neural network model, employing a radial basis function and a sigmoid multi-layer perceptron sub-networks, is capable of maximizing such an energy form locally, thus performing almost perfectly nearest neighbor classification, when initiated by a distorted pattern. The proposed design scheme allows for explicit representation of prototype patterns as network parameters, as well as augmenting additional or forgetting existing memory patterns. The dynamical classification scheme implemented by the network eliminates all comparisons, which are the vital steps of the conventional nearest neighbor classification process. The performance of the proposed network model is demonstrated on binary and gray-scale image reconstruction applications.