Learning with generalized-mean neuron model

The artificial neuron has come a long way in modeling the functional capabilities of various neuronal processes. The higher order neurons have shown improved computational power and generalization ability. However, these models are difficult to train because of a combinatorial explosion of higher order terms as the number of inputs to the neuron increases. This work presents an artificial neural network using a neuron architecture called generalized mean neuron (GMN) model. This neuron model consists of an aggregation function which is based on the generalized mean of the all the inputs applied to it. The proposed neuron model with same number of parameters as the McCulloch–Pitts model demonstrates better computational power. The performance of this model has been benchmarked on both classification and time series prediction problems.