The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and
simplest neural network models. However, it is incapable of classifying linearly inseparable …

Artificial neural networks (ANNs) are comparatively straightforward to understand and use in
the analysis of scientific data. However, this relative transparency may encourage their use …

This review considers the application of artificial neural networks (ANNs) to rainfall-runoff
modelling and flood forecasting. This is an emerging field of research, characterized by a …

In this paper, learning algorithms for radial basis function (RBF) networks are discussed.
Whereas multilayer perceptrons (MLP) are typically trained with backpropagation …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

Forecasting a hydrologic time series has been one of the most complicated tasks owing to
the wide range of data, the uncertainties in the parameters influencing the time series and …

In this paper, a new globally convergent modification of the Resilient Propagation-Rprop
algorithm is presented. This new addition to the Rprop family of methods builds on a …

This article focuses on gradient-based backpropagation algorithms that use either a
common adaptive learning rate for all weights or an individual adaptive learning rate for …

In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming
(HDP), dual heuristic programming (DHP), and their action-dependent ones, have been …

Networks of spiking neurons can perform complex non-linear computations in fast temporal
coding just as well as rate coded networks. These networks differ from previous models in …