A neural network which learns decision boundaries with nonlinear clustering

Presents a novel neural network which works as a classifier. It uses Euclidean distance similarity measurement to form clusters which are represented by output units. Uniquely, output units in the proposed network have nonlinear hard-limiter activation functions. Through this nonlinear activation function, complex decision boundaries from input patterns can be approximated. Furthermore, it does not forget previously remembered training patterns as it remembers newly shown patterns. This is shown with illustrative proofs. Simulation results are presented and compared with those from the backpropagation neural network. They demonstrate that the network described, with its simple architecture and learning, it is able to capture continuous distributions of complex decision boundaries from discrete patterns