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The paper proposes new neuron architecture for Neural Network models with an aggregation function based on geometric mean of all inputs. This new neuron model gives better accuracy compared to Multilayer Perceptron model (MLP) without increasing the number of parameters. Various error measures have been used with this model. The effectiveness of this model(More)
The paper proposes a novel neuron model termed as Generalized Power Mean Neuron model (GPMN). The paper focuses on illustrating the computational power and the generalization capability of this model. In this model, the aggregation function is based on generalized power mean of the inputs. The performance of the neural network using GPMN model is compared(More)
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