Riemannian metrics on an infinite dimensional symplectic group

The aim of this paper is the geometric study of the symplectic operators which are a perturbation of the identity by a Hilbert-Schmidt operator. This subgroup of the symplectic group was introduced in Pierre de la Harpe's classical book of Banach-Lie groups. Throughout this paper we will endow the tangent spaces with different Riemannian metrics. We will use the minimal curves of the unitary group and the positive invertible operators to compare the length of the geodesic curves in each case. Moreover we will study the completeness of the symplectic group with the geodesic distance.Comment: 18 pages. The final version of this preprint will appear in Journal of Mathematical Analysis and Application

Applications of recurrent neural networks in batch reactors. Part II: Nonlinear inverse and predictive control of the heat transfer fluid temperature

Although nonlinear inverse and predictive control techniques based on artificial neural networks have been extensively applied to nonlinear systems, their use in real time applications is generally limited. In this paper neural inverse and predictive control systems have been applied to the real-time control of the heat transfer fluid temperature in a pilot chemical reactor. The training of the inverse control system is carried out using both generalised and specialised learning. This allows the preparation of weights of the controller acting in real-time and appropriate performances of inverse neural controller can be achieved. The predictive control system makes use of a neural network to calculate the control action. Thus, the problems related to the high computational effort involved in nonlinear model-predictive control systems are reduced. The performance of the neural controllers is compared against the self-tuning PID controller currently installed in the plant. The results show that neural-based controllers improve the performance of the real plant.Publicad

Multi-step learning rule for recurrent neural models: an application to time series forecasting

Multi-step prediction is a difficult task that has attracted increasing interest in recent years. It tries to achieve predictions several steps ahead into the future starting from current information. The interest in this work is the development of nonlinear neural models for the purpose of building multi-step time series prediction schemes. In that context, the most popular neural models are based on the traditional feedforward neural networks. However, this kind of model may present some disadvantages when a long-term prediction problem is formulated because they are trained to predict only the next sampling time. In this paper, a neural model based on a partially recurrent neural network is proposed as a better alternative. For the recurrent model, a learning phase with the purpose of long-term prediction is imposed, which allows to obtain better predictions of time series in the future. In order to validate the performance of the recurrent neural model to predict the dynamic behaviour of the series in the future, three different data time series have been used as study cases. An artificial data time series, the logistic map, and two real time series, sunspots and laser data. Models based on feedforward neural networks have also been used and compared against the proposed model. The results suggest than the recurrent model can help in improving the prediction accuracy.Publicad

Applications of recurrent neural networks in batch reactors. Part I: NARMA modelling of the dynamic behaviour of the heat transfer fluid

This paper is focused on the development of nonlinear models, using artificial neural networks, able to provide appropriate predictions when acting as process simulators. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. Different structures of NARMA (Non-linear ARMA) models have been studied. The experimental results have allowed to carry out a comparison between the different neural approaches and a first-principles model. The best neural results are obtained using a parallel model structure based on a recurrent neural network architecture, which guarantees better dynamic approximations than currently employed neural models. The results suggest that parallel models built up with recurrent networks can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits which change from batch installation to installation.Publicad

Artkino Pictures Argentina: a Window to the Communist Europe in Buenos Aires Screens (1954-1970)

The aim of this paper is to study a specific aspect of the Communist foreign propaganda policy in Latin America: its cThe aim of this paper is to study a specific aspect of the Communist foreign propaganda policy in Latin America: its cultural influence through the export of films. Renewed after Stalin´s death, Soviet cultural propaganda concentrated on gaining the favour of foreign public. Particularly in Argentina, several propaganda techniques were implemented. Although the export of films was just one of them, it soon became very successful thanks to the collaboration of a local cultural mediator, the film distribution company Artkino Pictures, as well as its owner and founder, Argentino Vainikoff. His expertise in the field actually gained him a new business deal with Czechoslovak filmography, which somewhat contested USSR imagery. In all, here –with the aid of oral history as well as contemporary press analysis– we argue that Artkino´s role in the import of an idealised imaginary of Communism was crucial and had a particularly strong impact on middle-class citizens of the cultural and artistic regional centre that Buenos Aires was in the 1950s and 1960s, and from where all Latin America, as the Soviets soon acknowledged, could be reached.ultural influence through the export of films. Renewed after Stalin´s death, Soviet cultural propaganda concentrated on gaining the favour of foreign public. Particularly in Argentina, several propaganda techniques were implemented. Although the export of films was just one of them, it soon became very successful thanks to the collaboration of a local cultural mediator, the film distribution company Artkino Pictures, as well as its owner and founder, Argentino Vainikoff. His expertise in the field actually gained him a new business deal with Czechoslovak filmography, which somewhat contested USSR imagery. In all, here –with the aid of oral history as well as contemporary press analysis– we argue that Artkino´s role in the import of an idealised imaginary of Communism was crucial and had a particularly strong impact on middle-class citizens of the cultural and artistic regional centre that Buenos Aires was in the 1950s and 1960s, and from where all Latin America, as the Soviets soon acknowledged, could be reached.Fil: Galván, Maria Valeria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto de Historia Argentina y Americana ; ArgentinaFil: Zourek, Michal. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto de Historia Argentina y Americana ; Argentina. Institute of Technology and Business in České Budějovice; República Chec

Optimal Scheduling of Energy Storage Using A New Priority-Based Smart Grid Control Method

This paper presents a method to optimally use an energy storage system (such as a battery) on a microgrid with load and photovoltaic generation. The purpose of the method is to employ the photovoltaic generation and energy storage systems to reduce the main grid bill, which includes an energy cost and a power peak cost. The method predicts the loads and generation power of each day, and then searches for an optimal storage behavior plan for the energy storage system according to these predictions. However, this plan is not followed in an open-loop control structure as in previous publications, but provided to a real-time decision algorithm, which also considers real power measures. This algorithm considers a series of device priorities in addition to the storage plan, which makes it robust enough to comply with unpredicted situations. The whole proposed method is implemented on a real-hardware test bench, with its different steps being distributed between a personal computer and a programmable logic controller according to their time scale. When compared to a different state-of-the-art method, the proposed method is concluded to better adjust the energy storage system usage to the photovoltaic generation and general consumption.Unión Europea ID 100205Unión Europea ID 26937

AMPSO: A new Particle Swarm Method for Nearest Neighborhood Classification

Nearest prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper, we first use the standard particle swarm optimizer (PSO) algorithm to find those prototypes. Second, we present a new algorithm, called adaptive Michigan PSO (AMPSO) in order to reduce the dimension of the search space and provide more flexibility than the former in this application. AMPSO is based on a different approach to particle swarms as each particle in the swarm represents a single prototype in the solution. The swarm does not converge to a single solution; instead, each particle is a local classifier, and the whole swarm is taken as the solution to the problem. It uses modified PSO equations with both particle competition and cooperation and a dynamic neighborhood. As an additional feature, in AMPSO, the number of prototypes represented in the swarm is able to adapt to the problem, increasing as needed the number of prototypes and classes of the prototypes that make the solution to the problem. We compared the results of the standard PSO and AMPSO in several benchmark problems from the University of California, Irvine, data sets and find that AMPSO always found a better solution than the standard PSO. We also found that it was able to improve the results of the Nearest Neighbor classifiers, and it is also competitive with some of the algorithms most commonly used for classification.This work was supported by the Spanish founded research Project MSTAR::UC3M, Ref: TIN2008-06491-C04-03 and CAM Project CCG06-UC3M/ESP-0774.Publicad