Add to ORKGA comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training, generalization, or computational costs, are analyzed, both in general-purpose and in embedded computing environments. Finally, a strategy to convert a network configuration between different activation functions without altering the network mapping capabilities will be presented
An activation function, possibly new, is proposed for use in digital simulation of arti#cial neural ...
Artificial neural network learning is typically accomplished via adaptation between neurons. This pa...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
A comprehensive review on the problem of choosing a suitable activation function for the hidden laye...
Copyright © 2015 Antonino Laudani et al. This is an open access article distributed under the Creati...
Artificial neural networks are function-approximating models that can improve themselves with experi...
Artificial Neural Networks (ANNs) are one of the most comprehensive tools for  classification. In t...
In this paper we describe several different training algorithms for feed forward neural networks(FFN...
In this dissertation the problem of the training of feedforward artificial neural networks and its a...
Activation functions used in hidden layers directly affect the possibilities for describing nonlinea...
In the paper, an ontogenic artificial neural network (ANNs) is proposed. The network uses orthogonal...
© 2018 IEEE. Artificial feedforward neural networks for simple objects recognition of different conf...
The activation function deployed in a deep neural network has great influence on the performance of ...
In neural networks literature, there is a strong interest in identifying and defining activation fun...
In this paper the effects of different activation functions on neural networks are argued
An activation function, possibly new, is proposed for use in digital simulation of arti#cial neural ...
Artificial neural network learning is typically accomplished via adaptation between neurons. This pa...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
A comprehensive review on the problem of choosing a suitable activation function for the hidden laye...
Copyright © 2015 Antonino Laudani et al. This is an open access article distributed under the Creati...
Artificial neural networks are function-approximating models that can improve themselves with experi...
Artificial Neural Networks (ANNs) are one of the most comprehensive tools for  classification. In t...
In this paper we describe several different training algorithms for feed forward neural networks(FFN...
In this dissertation the problem of the training of feedforward artificial neural networks and its a...
Activation functions used in hidden layers directly affect the possibilities for describing nonlinea...
In the paper, an ontogenic artificial neural network (ANNs) is proposed. The network uses orthogonal...
© 2018 IEEE. Artificial feedforward neural networks for simple objects recognition of different conf...
The activation function deployed in a deep neural network has great influence on the performance of ...
In neural networks literature, there is a strong interest in identifying and defining activation fun...
In this paper the effects of different activation functions on neural networks are argued
An activation function, possibly new, is proposed for use in digital simulation of arti#cial neural ...
Artificial neural network learning is typically accomplished via adaptation between neurons. This pa...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...