Learning of geometric mean neuron model using resilient propagation algorithm

The paper proposes a new neuron model (geometric mean neuron model) with an aggregation function based on geometric mean of all inputs. Performance of the geometric mean neuron model was evaluated using various learning algorithms like the back-propagation and resilient propagation on various real life data sets. Comparison of the performance of this model was made with the performance of multilayer perceptron. It has been shown that the geometric mean based aggregation function with resilient propagation (RPROP) performs the best both in terms of accuracy and speed.