L'objectiu principal del grup és avançar en les metodologies que conformen l'àrea de la infor-màtica tova (Soft Computing), així com investigar possibles hibridacions d'aquestes amb l'ob-jectiu de millorar-ne el rendiment i la fiabilitat. També és un objectiu important aplicar les metodologies desenvolupades a problemes reals en àrees com la medicina, ecologia, farmacoprote-òmica, e-learning, etc. El grup ha publicat més de 150 articles de revista, més de 300 articles en congressos, 50 capítols de llibres i 60 reports de recerca.

http://futur.upc.edu/SOCO

The term "soft computing" was coined in the nineties, and it describes the combined use of a variety of computational approaches that have been developed over the last few decades, which include but are not limited to fuzzy systems, neural networks and evolutionary algorithms.

Despite their obvious differences, a common trait of these fields is the abandonment of binary logic, static analytical models, rigid classifications and deterministic searches. In an ideal problem description, the systems that require modelling and/or control would be defined completely and precisely. In this case, formal reasoning systems can be used to associate Boolean values to state descriptions or physical systems' behaviour. Nevertheless, when tackling real-world problems, it is not unusual to find them incompletely or badly defined, which makes it difficult to model them and requires large search spaces. As a result, precise models, should they exist, might turn out to be impractical and/or costly. Usually, the relevant information that is available is presented either as empirical, a priori knowledge or as input-output instance descriptions of the systems' behaviour. This makes the use of approximate reasoning systems necessary, as they can flexibly cope with such far-from-perfect information.

The main goal of the SOCO group is to make progress in the state-of-the-art development of soft-computing methodologies, as well as to research their possible hybridisation in order to improve their robustness and efficacy. The SOCO group is currently working on the following research lines:

* Feature selection and dimensionality reduction

* Fuzzy systems (Fuzzy Inductive Reasoning, FIR)

* Artificial neural networks (feed-forward, recurrent, heterogeneous)

* Unsupervised probabilistic models

* Genetic algorithms and evolutionary strategies

* Pattern recognition and computer vision

* Hybrid soft-computing methods, including the following:

- Neural networks and support vector machines

- Fuzzy Inductive Reasoning and simulated annealing

- Fuzzy Inductive Reasoning and genetic algorithms

- Frequency selection for neural networks

- Cooperation of local experts for inductive reasoning

- Incremental construction of hybrid recurrent neural networks

The application of these methodologies to real-world problems is also one of the group's goals. The group has carried out research in the following areas of application:

* Medical (human central nervous system, cancer prediction, diagnosis, cognitive neuroscience, etc.)

* Biological (growth of white shrimp)

* Ecological (analysis of pollutant concentration in urban areas and ecological status modelling of streams)

http://futur.upc.edu/SOCO

The term "soft computing" was coined in the nineties, and it describes the combined use of a variety of computational approaches that have been developed over the last few decades, which include but are not limited to fuzzy systems, neural networks and evolutionary algorithms.

Despite their obvious differences, a common trait of these fields is the abandonment of binary logic, static analytical models, rigid classifications and deterministic searches. In an ideal problem description, the systems that require modelling and/or control would be defined completely and precisely. In this case, formal reasoning systems can be used to associate Boolean values to state descriptions or physical systems' behaviour. Nevertheless, when tackling real-world problems, it is not unusual to find them incompletely or badly defined, which makes it difficult to model them and requires large search spaces. As a result, precise models, should they exist, might turn out to be impractical and/or costly. Usually, the relevant information that is available is presented either as empirical, a priori knowledge or as input-output instance descriptions of the systems' behaviour. This makes the use of approximate reasoning systems necessary, as they can flexibly cope with such far-from-perfect information.

The main goal of the SOCO group is to make progress in the state-of-the-art development of soft-computing methodologies, as well as to research their possible hybridisation in order to improve their robustness and efficacy. The SOCO group is currently working on the following research lines:

* Feature selection and dimensionality reduction

* Fuzzy systems (Fuzzy Inductive Reasoning, FIR)

* Artificial neural networks (feed-forward, recurrent, heterogeneous)

* Unsupervised probabilistic models

* Genetic algorithms and evolutionary strategies

* Pattern recognition and computer vision

* Hybrid soft-computing methods, including the following:

- Neural networks and support vector machines

- Fuzzy Inductive Reasoning and simulated annealing

- Fuzzy Inductive Reasoning and genetic algorithms

- Frequency selection for neural networks

- Cooperation of local experts for inductive reasoning

- Incremental construction of hybrid recurrent neural networks

The application of these methodologies to real-world problems is also one of the group's goals. The group has carried out research in the following areas of application:

* Medical (human central nervous system, cancer prediction, diagnosis, cognitive neuroscience, etc.)

* Biological (growth of white shrimp)

* Ecological (analysis of pollutant concentration in urban areas and ecological status modelling of streams)

http://futur.upc.edu/SOCO

Enviaments recents

  • Tradares: A tool for the automatic evaluation of human translation quality within an MOOC environment 

    Betanzos, Miguel; Ruiz Costa-Jussà, Marta; Belanche Muñoz, Luis Antonio (2017-05-13)
    Article
    Accés restringit per política de l'editorial
    In this paper, we introduce TradARES, a tool for the automatic evaluation of human translation quality developed in the context of an OpenEdx MOOC (Massive Open Online Course), setting the foundation for a tool that provides ...
  • Intelligent data analysis approaches to churn as a business problem: a survey 

    García Gómez, David; Nebot Castells, M. Àngela; Vellido Alcacena, Alfredo (2017-06)
    Article
    Accés restringit per política de l'editorial
    Globalization processes and market deregulation policies are rapidly changing the competitive environments of many economic sectors. The appearance of new competitors and technologies leads to an increase in competition ...
  • Bayesian semi non-negative matrix factorisation 

    Vilamala Muñoz, Albert; Vellido Alcacena, Alfredo; Belanche Muñoz, Luis Antonio (I6doc.com, 2016)
    Text en actes de congrés
    Accés obert
    Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow ...
  • Modeling and robust low level control of an omnidirectional mobile robot 

    Comasòlivas Font, Ramon; Quevedo Casín, Joseba Jokin; Escobet Canal, Teresa; Escobet Canal, Antoni; Romera Formiguera, Juli (2017-04-01)
    Article
    Accés restringit per política de l'editorial
    This paper presents the modeling and robust low-level control design of a redundant mobile robot with four omnidirectional wheels, the iSense Robotic (iSRob) platform, that was designed to test safe control algorithms. ...
  • A methodological approach for algorithmic composition systems' parameter spaces aesthetic exploration 

    Paz Ortiz, Iván; Nebot Castells, M. Àngela; Romero Merino, Enrique; Múgica Álvarez, Francisco; Vellido Alcacena, Alfredo (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    Comunicació de congrés
    Accés obert
    Algorithmic composition is the process of creating musical material by means of formal methods. As a consequence of its design, algorithmic composition systems are (explicitly or implicitly) described in terms of parameters. ...
  • A decision making support tool: The resilience management fuzzy controller 

    González Cardenas, Rubén; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Vellido Alcacena, Alfredo (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    Text en actes de congrés
    Accés obert
    In this paper a fuzzy controller capable to perform an automated estimation of the period of time necessary to recover a resilience level is proposed. Estimations where made by considering realistic time-dependent action ...
  • A model for continuous monitoring of patients with major depression in short and long term periods 

    Múgica Álvarez, Francisco; Nebot Castells, M. Àngela; Bagherpour, Solmaz; Baladón, Luisa; Serrano, Antoni (2016-12-19)
    Article
    Accés obert
    BACKGROUND AND OBJECTIVE: Major depressive disorder causes more human suffering than any other disease affecting humankind. It has a high prevalence and it is predicted that it will be among the three leading causes of ...
  • Multivariate dynamic kernels for financial time series forecasting 

    Peña, Mauricio; Arratia Quesada, Argimiro Alejandro; Belanche Muñoz, Luis Antonio (Springer, 2016)
    Text en actes de congrés
    Accés obert
    We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data ...
  • Fuzzy inductive reasoning forecasting strategies able to cope withmissing data: A smart grid application 

    Jurado, Sergio; Nebot Castells, M. Àngela; Múgica Álvarez, Francisco; Mihaylov, Mihail (2017-02)
    Article
    Accés restringit per política de l'editorial
    Dealing with missing data is of great practical and theoretical interest in forecasting applications. In this study, we deal with the problem of forecasting with missing data in smart grid and BEMS applications, where the ...
  • ECG assessment based on neural networks with pretraining 

    Ribas Ripoll, Vicent; Wojdel, Anna; Romero Merino, Enrique; Ramos, Pablo; Brugada Terradellas, Josep (2016-12-01)
    Article
    Accés restringit per política de l'editorial
    In this paper, we present a new automatic screening method to assess whether a patient from ambulatory care or emergency should be referred to a cardiology service. This method is based on deep neural networks with pretraining ...

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