M. E. Gegúndez

Identification of piecewise affine systems by means of fuzzy clustering and competitive learning

This paper presents an identification method for a class of dynamic system known as piecewise affine systems. Such systems are composed of a set of affine maps which relate inputs and outputs. These maps are defined in disjunctive regions in the regression space, itself composed of system inputs and outputs. The aim of the proposed method is to obtain a model of the system from a set of input-output data. This model comprises a set of submodels defined in different regions of the regression space. The proposed method is sequenced according to several stages which identify the set of submodels and the regions in which they are defined. These submodels are obtained by means of an algorithm inspired by competitive learning which rewards those that best fit the data in each region of the regression space. The method uses a process of fuzzy clustering in order to obtain a subset of representatives from the original data set, so reducing the amount of information to be processed while retaining the significant information from the original data and minimizing the effect of noise on the data.

A New Self-adaptative Crossover Operator for Real-Coded Evolutionary Algorithms

In this paper we propose a new self-adaptative crossover operator for real coded evolutionary algorithms. This operator has the capacity to simulate other real-coded crossover operators dynamically and, therefore, it has the capacity to achieve exploration and exploitation dynamically during the evolutionary process according to the best individuals. In other words, the proposed crossover operator may handle the generational diversity of the population in such a way that it may either generate additional population diversity from the current one, allowing exploration to take effect, or use the diversity previously generated to exploit the better solutions. In order to test the performance of this crossover, we have used a set of test functions and have made a comparative study of the proposed crossover against other classic crossover operators. The analysis of the results allows us to affirm that the proposed operator has a very suitable behavior; although, it should be noted that it offers a better behavior applied to complex search spaces than simple ones.

How do foreign income shocks affect the magnitude of Spanish tourism

This article analyses how income shocks in nine countries with major tourism flows to Spain affect the Spanish tourism arrivals for the period 2000–2017. To this end, we apply a Granger causality analysis based on augmented vector autoregressive (VAR) model in levels and extra lags. This provides more efficient and robust results than the standard VAR model that can lead to biased results with limited samples, especially in the country-by-country analysis. In this article, we present the first application of these econometric techniques in the field by studying the relationship between tourism and economic growth. Empirical results suggest that the impact of gross domestic product in the origin countries of inbound Spanish tourism is heterogeneous and country specific, and asymmetric behaviours appear among countries. The analysis of this issue is relevant for the design and implementation of tourism promotion programmes specific to the country of origin by policymakers and practitioners.

A General Framework for Predictors Based on Bounding Techniques and Local Approximation

This paper introduces a general framework for prediction based on nonparametric local estimation and bounding techniques. A set of historic input-output measurements of the system is stored in a database. When a prediction for a given point is required, data from the neighborhood of this point is retrieved and a prediction is formed. These prediction methods return an interval that bounds the considered system output. The width of the obtained interval prediction reflects the amount of information about the system available at the point to be predicted. In addiction, the midpoint of the interval prediction can be used as central estimate. The contribution of the paper is threefold. First, a general framework that covers previous methods proposed in the literature is presented. Second, the general properties of the framework are analyzed. Third, new predictors based on this framework are proposed. Finally, a benchmark example and a comparative study are provided for illustration purposes.

Combined stochastic and deterministic interval predictor for time-varying systems

This work proposes a new interval predictor for time-varying linear systems. An interval predictor is a method that provides an interval as outer estimation of the future system output. The center of the interval prediction can be used as point or nominal prediction. This interval center is obtained by a linear combination of stored past outputs. The interval width is obtained using an outer bound of the prediction error. Two different approaches have been considered in literature, based on deterministic and stochastic assumptions respectively. The novelty of this work is to use a combined deterministic and stochastic assumption on this bound to obtain the interval prediction. The aim is to achieve a low error in the central prediction and a small interval width. An example is provided to illustrate the improvement provided by the proposed predictor.

Interval predictor based on a Reversed Huber's error function

In dynamical systems context, a predictor is a method that provides an estimation of the future system output using past information of the system. An interval predictor provides an outer estimation of the future output. The center of this interval can be used as central or nominal prediction. A method to formulate interval predictors is to assume an unknown but bounded error in the system measurements. The aim of this work is to study the benefits of using a Reversed Hubers function as error function in this kind of predictors. A Reversed Hubers function is a convex function, piecewise linear near zero but quadratic for large values. The paper provides a nonparametric formulation of the interval predictor and shows by a real world example that the proposed predictor can improve the performance of the central prediction.

Robust predictor for nonlinear systems based on bounding-error methods

A new robust predictor for nonlinear systems is proposed. The predictor uses a set of system input-output measurements and a local linearization method based on bounded-error to return an interval that bounds the system output. The midpoint of the prediction interval is the optimal solution of an optimization problem which minimizes a quadratic prediction-error functional cost with a regularization term. The width of the prediction interval can be used as a reliability index of this central prediction. Bounded-error methods use an unique error bound applied to all measurements. The main idea of this work is to use a reliability index that provides a different error bound for each measurement. This allows us to apply the proposed method to measurements with outliers or different error bounds. The main contribution of the paper is the explicit expression that provides the prediction interval and assures a low computational effort.

Gpax: Genetic Parabolic Adaptive Crossover Operator

Abstract In this paper we propose a new crossover operator for real coded evolutionary algorithms that is based on a parabolic probability density function. This density function depends on two real parameters α and β which have the capacity to achieve exploration and exploitation dynamically during the evolutionary process in relation to the best individuals. In other words, the proposed crossover operator is able to handle the generational diversity of the population in such a way that it can either generate additional population diversity, therefore allowing exploration to take effect, or use the diversity previously generated to exploit the better solutions. In order to test the performance of this crossover, we have used a set of test functions and have made a comparative study of the proposed crossover against other classic crossover operators. The analysis of the results allows us to affirm that the proposed operator displays a very suitable behavior, although, it should be noted that it offers a b...