Differential learning algorithm for Artificial Neural Networks

Artificial Neural Networks (ANN) are extremely useful to relate the nonlinearly depending outputs with the inputs. Various architectures are available for the ANNs to speedup the training period and reduce the square error. In this paper, new classes of neural networks with differential feedback are presented. The different orders of differential feed back form a manifold of hyperplanes. Interesting properties of this differentially fed ANN (DANN) are derived through these hyperplanes.