Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

We propose an automatic methodology framework for short- and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays a key role in driving the identification and learning procedures. Concrete criteria and procedures within the proposed methodology framework are applied to a number of time series prediction problems. The learn from examples method introduced by Wang and Mendel (W&M) is used for identification. The Levenberg–Marquardt (L–M) optimization method is then applied for tuning. The W&M method produces compact and potentially accurate inference systems when applied after a proper variable selection stage. The L–M method yields the best compromise between accuracy and interpretability of results, among a set of alternatives. Delta test based residual variance estimations are used in order to select the best subset of inputs to the fuzzy inference systems as well as the number of linguistic labels for the inputs. Experiments on a diverse set of time series prediction benchmarks are compared against least-squares support vector machines (LS-SVM), optimally pruned extreme learning machine (OP-ELM), and k-NN based autoregressors. The advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications.Ministerio de Ciencia e Innovación TEC2008-04920Junta de Andalucía P08-TIC-03674, IAC07-I-0205:33080, IAC08-II-3347:5626

Thing Complex Fuzzy Systems by Supervised Learning Algorithms

Tuning a fuzzy system to meet a given set of inpuffoutput patterns is usually a difficult task that involves many parameters. This paper presents an study of different approaches that can be applied to perform this tuning process automatically, and describes a CAD tool, named xfsl, which allows applying a wide set of these approaches: (a) a large number of supervised learning algorithms; (b) different processes to simplify the learned system; (c) tuning only specific parameters of the system; (d) the ability to tune hierarchical fuzzy systems, systems with continuous output (like fuzzy controller) as well as with categorical output (like fuzzy classifiers), and even systems that employ user-defined fuzzy functions; and, finally, (e) the ability to employ this tuning within the design flow of a fuzzy system, because xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0.Comisión Interministerial de Ciencia y Tecnología TIC2001-1726-C02-0

XFSL: A tool for supervised learning of fuzzy systems

This paper presents Xfsl, a tool for the automatic tuning of fuzzy systems using supervised learning algorithms. The tool provides a wide set of learning algorithms, which can be used to tune complex systems. An important issue is that Xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0, and hence, it can be easily employed within the design flow of a fuzzy system.Comisión Interministerial de Ciencia y Tecnología TIC98-0869Fondo Europeo de Desarrollo Regional 1FD97-0956-C3-0

Multivariate Approaches to Classification in Extragalactic Astronomy

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono-or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.Comment: Open Access paper. http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>. \<10.3389/fspas.2015.00003 \&g

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules