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

  • 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 ...
  • Glucose oxidase biosensor modeling and predictors optimization by machine learning methods 

    González Navarro, Félix Fernando; Stilianova Stoytcheva, Margarita; Rentería Gutiérrez, Livier; Belanche Muñoz, Luis Antonio; Flores Ríos, Brenda L.; Ibarra Esquer, Jorge E. (2016-11-01)
    Article
    Accés obert
    Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides ...
  • Analyzing the amperometric response of a glucose oxidase sensor applying mathematical models 

    González Navarro, Félix Fernando; Stilianova Stoytcheva, Margarita; Belanche Muñoz, Luis Antonio; Flores Ríos, Brenda L.; Ibarra Esquer, Jorge E.; Rentería Gutiérrez, Livier; López Morteo, Gabriel A. (2016-12-01)
    Article
    Accés restringit per política de l'editorial
    Background: The biosensors are analytical devices combining a bioreceptor and a physicochemical transducer to translate the signal resulting from the interaction of the analyte with the biological element into an electrical ...
  • Similarity networks for classification: a case study in the Horse Colic problem 

    Belanche Muñoz, Luis Antonio; Hernández González, Jerónimo (2014)
    Report de recerca
    Accés obert
    This paper develops a two-layer neural network in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic ...
  • Exploiting the accumulated evidence for gene selection in microarray gene expression data 

    Prat, Gabriel; Belanche Muñoz, Luis Antonio (2013)
    Report de recerca
    Accés obert
    Machine Learning methods have of late made signicant efforts to solving multidisciplinary problems in the field of cancer classification using microarray gene expression data. Feature subset selection methods can play an ...
  • Automated quality control for proton magnetic resonance spectroscopy data using convex non-negative matrix factorization 

    Mocioiu, Victor; Kyathanahally, Sreenath P.; Arús, Carles; Vellido Alcacena, Alfredo; Julià Sapé, Margarida (Springer, 2016)
    Text en actes de congrés
    Accés restringit per política de l'editorial
    Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic ...
  • Similarity and dissimilarity concepts in machine learning 

    Orozco Luquero, Jorge (2004-02)
    Report de recerca
    Accés obert
    Similarity and dissimilarity are rarely formalized concepts in Artificial Intelligence (AI). Similarity and dissimilarity have a psychological origin, and they have been adapted to AI. In this field, however, similarity ...
  • Studying embedded human EEG dynamics using generative topographic mapping 

    Vellido Alcacena, Alfredo; El-Deredy, W.; Lisboa, Paulo J G (2004-02)
    Report de recerca
    Accés obert
    A method has recently been proposed [1] to extract multiple signal source information from single-channel electroencephalogram (EEG) recordings. A dynamical systems approach is used to analyze the resulting EEG time series, ...
  • Exploring dopamine-mediated reward processing through the analysis of EEG-measured gamma-band brain oscillations 

    Vellido Alcacena, Alfredo; El-Deredy, W. (2004-02)
    Report de recerca
    Accés obert
    The central role of the dopamine system on reward brain processing is now quite well delimited. Its influence on other brain areas for learning and decision-making is still a matter of intense research. Most of this is ...

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