Multilayer feedforward networks are universal approximators
Neural Networks
Approximation by ridge functions and neural networks with one hidden layer
Journal of Approximation Theory
A feedforward neural network with function shape autotuning
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Networks with trainable amplitude of activation functions
Neural Networks
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
IEEE Transactions on Neural Networks
Approximation capability in C(R¯n) by multilayer feedforward networks and related problems
IEEE Transactions on Neural Networks
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
| Hi-index | 0.01 |
The role of activation functions in feedforward artificial neural networks has not been investigated to the desired extent. The commonly used sigmoidal functions appear as discrete points in the sigmoidal functional space. This makes comparison difficult. Moreover, these functions can be interpreted as the (suitably scaled) integral of some probability density function (generally taken to be symmetric/bell shaped). Two parameterization methods are proposed that allow us to construct classes of sigmoidal functions based on any given sigmoidal function. The suitability of the members of the proposed class is investigated. It is demonstrated that all members of the proposed class(es) satisfy the requirements to act as an activation function in feedforward artificial neural networks.