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This paper addresses the problem of using Hopfield Neural Networks (HNNs) for on-line parameter estimation. As presented here, a HNN is a nonautonomous nonlinear dynamical system able to produce a time-evolving estimate of the actual parameterization. The stability analysis of the HNN is carried out under more general assumptions than those previously considered in the literature, yielding a weaker sufficient condition under which the estimation error asymptotically converges to zero. Furthermore, a robustness analysis is made, showing that, under the presence of perturbations, the estimation error converges to a bounded neighbourhood of zero, whose size decreases with the size of the perturbations. The results obtained are illustrated by means of two case studies, where the HNN is compared with two other methods. [Copyright &y& Elsevier]

A general version of a hybrid method for parameter estimation is presented with a theoretical support and an illustrative example of application. This method consists of a curve fitting algorithm that takes the initial estimate of the parameterization from an artificial neural network. The idea is to improve the convergence of the algorithm to the sought parameterization using a close initial estimate. The motivation arises from biomedical problems where one is interested in obtaining a meaningful estimate so that it can be used for both description and prediction purposes. Two strategies are proposed for the application of the hybrid method: one is of general applicability, the other is intended for systems defined by the series connection of various blocks. The feasibility of the method is illustrated with a case study related to the neuromuscular blockade of patients undergoing general anaesthesia. [Copyright &y& Elsevier]

Abstract: Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the classification of nominal classes where the order relation is ignored. This paper introduces a new machine learning paradigm intended for multi-class classification problems where the classes are ordered. The theoretical development of this paradigm is carried out under the key idea that the random variable class associated with a given query should follow a unimodal distribution. In this context, two approaches are considered: a parametric, where the random variable class is assumed to follow a specific discrete distribution; a nonparametric, where the random variable class is assumed to be distribution-free. In either case, the unimodal model can be implemented in practice by means of feedforward neural networks and support vector machines, for instance. Nevertheless, our main focus is on feedforward neural networks. We also introduce a new coefficient, , to measure the performance of ordinal data classifiers. An experimental study with artificial and real datasets is presented in order to illustrate the performances of both parametric and nonparametric approaches and compare them with the performances of other methods. The superiority of the parametric approach is suggested, namely when flexible discrete distributions, a new concept introduced here, are considered. [Copyright &y& Elsevier]

Abstract: The transfer of reactive bromine into the atmosphere was recently observed by Hönninger et al. [Hönninger, G., Bobrowski, N., Palenque, E.R., Torrez, R., Platt, U., 2004. Reactive bromine and sulfur emission at salar de Uyuni, Bolivia. Geophys. Res. Lett. 31, doi:10.1029/2003GL018818] in a large salt pan of the Bolivian Altiplano: the salar de Uyuni. However, bromide is considered to be an excellent conservative tracer, which leads to the questioning of its actual conservation in surficial geochemical processes. The relation between bromide and lithium, thought to be a conservative component in waters and brines of the Central Andes, points to a depletion of Br relative to Li in Uyuni brines of about 50–300kg/day, a flux close to that measured by : ⩾200kg/day. Such values are very low in regard to the size of the salar (5–30g/km2/day). Salt efflorescences have a much higher surface area than a flat salt crust, which should enhance the release of bromine. Leach solutions of salt efflorescences in closed basins of northern Chile are compared to their parent waters. Conservative components should have the same concentration ratios in both solutions. Actually, a strong depletion in Br is observed in the leach solutions, which could suggest a significant release of Br from the salt into the atmosphere. During the rainy season, efflorescences are leached and their dissolved components brought in saline lakes and salars. Evaporative profiles show a slight but noticeable fractionation between Li and Br which could be due to the contribution of Br-depleted leach solutions of salt efflorescences. Therefore, bromide does not behave conservatively in surface waters and brines of the Central Andes. If the loss of Br is really due to its transfer into the atmosphere, then the flux would be much higher than that estimated for the salar de Uyuni alone. Numerous salt pans, saline lakes, and widespread efflorescences covering large land surfaces would contribute significantly more reactive bromine into the atmosphere. [Copyright &y& Elsevier]