Rough Neuron based on Pattern Space Partitioning

This paper proposes a novel method of combining Rough concepts with Neural Computation. The proposed New Rough Neuron consists of, one Lower Bound Neuron and another Boundary Neuron. The combination is designed in a way such that the Boundary Neuron deals only with the random and unpredictable part of the applied signal. This division results in an improved rate of error convergence in the back propagation of the neural network along with an improved parameter approximation during the network learning process. Some testing results have been presented, and performance compared with some of the prevalent designs.