Neural networks with asymmetric activation function for function approximation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Expert Systems with Applications: An International Journal
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The types of activation functions most often used in artificial neural networks are logistic and hyperbolic tangent. Activation functions used in ANN have been said to play an important role in the convergence of the algorithms used. This paper uses sigmoid functions in the processing units of neural networks. Such functions are commonly applied in statistical regression models. The nonlinear functions implemented here are the inverse of complementary log-log and probit link functions. A Monte Carlo framework is presented to evaluate the results of prediction power with these nonlinear functions.