The spectra for many common artificial neural network activation functions are derived,
including members of the sigmoid family, the Gaussian function, rectangular pulses and …

As hardware implementations of backpropagation and related training algorithms are
anticipated, the choice of a sigmoidal function should be carefully justified. Attention should …

Proposes a number of different implementations for the first derivative of the sigmoid
function. The implementation of the sigmoid function employs a powers-of-two piecewise …

Designing a neural network (NN) for processing complex signals is a challenging task due
to the lack of bounded and differentiable nonlinear activation functions in the entire complex …

The performance evaluation of a temporal sequence learning spiking neural network was
carried out. Neural network characteristics that were evaluated included: long temporal …

A neural structure which is comprised of dynamic neural units with time-varying sigmoidal
functions is proposed. The effect of sigmoidal gain on nonlinear dynamic systems is …

This paper presents a learning algorithm which may be used to train networks whose
neurons may have discontinuous or nondifferentiable activation functions. The algorithm has …

Training with additive input noise (jitter) in a commonly used heuristic for improving
generalization in layered perceptron artificial neural networks. A drawback of training with …

Summary form only given, as follows. A new neural network architecture called the
hierarchical neural network (HNN) is introduced. The HNN involves a number of stages in …

An innovative neural-based approach for function approximation is proposed by means of
the spectral analysis of the function y (x) to be approximated. Approximation is obtained by …