In neural networks literature, there is a strong interest in identifying and defining activation
functions which can improve neural network performance. In recent years there has been a …

Psychiatry today must gain a better understanding of the common and distinct
pathophysiological mechanisms underlying psychiatric disorders in order to deliver more …

We develop a non-intrusive reduced basis (RB) method for parametrized steady-state partial
differential equations (PDEs). The method extracts a reduced basis from a collection of high …

In this paper we demonstrate that finite linear combinations of compositions of a fixed,
univariate function and a set of affine functionals can uniformly approximate any continuous …

Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous
surge in research activities in the past decade. While ANNs provide a great deal of promise …

INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 1 Page 2
INTRODUCTION TO THE THEORY OF NEURAL COMPUTATION Page 3 Page 4 …

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled
Machine Vision) clearly and systematically presents the basic methodology of computer and …

The problem of the approximation of nonlinear mapping,(especially continuous mappings) is
considered. Regularization theory and a theoretical framework for approximation (based on …

This paper focuses on the promise of artificial neural networks in the realm of modelling,
identification and control of nonlinear systems. The basic ideas and techniques of artificial …

This paper informs a statistical readership about Artificial Neural Networks (ANNs), points
out some of the links with statistical methodology and encourages cross-disciplinary …