@conference {7737717,
title = {Battery diagnosis for electrical vehicles through semi-physical fuzzy models},
booktitle = {2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
year = {2016},
month = {July},
pages = {416-423},
abstract = {An intelligent model of a Li-Ion battery for electrical vehicles is proposed that allows for a fast battery health evaluation without the need of removing the battery from the vehicle. The only data needed for performing the condition monitoring are logged performance records (currents and voltages) that are commonly available at Battery Management Systems. The model comprises a combination of differential equations and fuzzy rule-based systems, these last being embedded as non-linear blocks in the differential equations. Fuzzy rules are learnt from data with the help of metaheuristics and fine tuned with gradient descent algorithms. A TensorFlow{\texttrademark} implementation of the learning algorithm has been developed that takes advantage of the numerical processing capabilities of a massively parallel GPU and improves the learning speed by a high margin.},
keywords = {Batteries, battery diagnosis, battery health evaluation, battery management systems, Battery models, battery powered vehicles, Computational modeling, differential equations, Discharges (electric), electrical vehicles, fuzzy models, Fuzzy rule-based systems, Fuzzy systems, gradient descent algorithms, graphics processing units, learning (artificial intelligence), learning algorithm, Li-FePO4batteries, Li-ion battery, Liquids, massively parallel GPU, Mathematical model, nonlinear blocks, Numerical models, parallel processing, power engineering computing, secondary cells, semiphysical fuzzy models, TensorFlow, TensorFlow{\texttrademark}, Vehicles},
doi = {10.1109/FUZZ-IEEE.2016.7737717},
author = {L. S{\'a}nchez and J. Otero and I. Couso and C. Blanco}
}
@conference {7338079,
title = {A software tool to efficiently manage the energy consumption of HPC clusters},
booktitle = {2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
year = {2015},
month = {Aug},
pages = {1-8},
abstract = {Today, High Performance Computing clusters (HPC) are an essential tool owing to they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nonetheless, HPC clusters consume large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, forcing owners to reduce operation costs. In this work we propose a software, named EECluster, to reduce the high energy consumption of HPC clusters. EECluster works with both OGE/SGE and PBS/TORQUE resource management systems and automatically tunes its decision-making mechanism based on a machine learning approach. The quality of the obtained results using this software are evaluated by means of experiments made using actual workloads from the Scientific Modelling Cluster at Oviedo University and the academic-cluster used by the Oviedo University for teaching high performance computing subjects.},
keywords = {academic-cluster, Computational modeling, Computer architecture, Decision making, decision-making mechanism, EECluster software tool, Fuzzy systems, Genetics, high performance computing clusters, HPC cluster energy consumption, learning (artificial intelligence), machine learning approach, OGE-SGE resource management systems, Oviedo University, parallel processing, PBS-TORQUE resource management systems, power aware computing, resource allocation, Scientific Modelling Cluster, software tools},
doi = {10.1109/FUZZ-IEEE.2015.7338079},
author = {A. Coca{\~n}a-Fern{\'a}ndez and L. S{\'a}nchez and J. Ranilla}
}
@conference {6706973,
title = {A first analysis of the effect of local and global optimization weights methods in the cooperative-competitive design of RBFN for imbalanced environments},
booktitle = {The 2013 International Joint Conference on Neural Networks (IJCNN)},
year = {2013},
month = {Aug},
pages = {1-8},
abstract = {Many real applications are composed of data sets where the distribution of the classes is significantly different. These data sets are commonly known as imbalanced data sets. Proposed approaches that address this problem can be categorized into two types: data-based, which resample problem data in a preprocessing phase and algorithm-based which modify or create new methods to address the imbalance problem. In this paper, CO2 RBFN a cooperative-competitive design method for Radial Basis Function Networks that has previously demonstrated a good behaviour tackling imbalanced data sets, is tested using two different training weights algorithms, local and global, in order to gain knowledge about this problem. As conclusions we can outline that a more global optimizer training algorithm obtains worse results.},
keywords = {Accuracy, Algorithm design and analysis, algorithm-based approach, CO2RBFN, cooperative-competitive design method for radial basis function networks, data-based approach, global optimization weights methods, global optimizer training algorithm, imbalanced data sets, learning (artificial intelligence), Least squares approximations, local optimization weights methods, Neurons, optimisation, radial basis function networks, Sociology, Training, training weights algorithms},
issn = {2161-4407},
doi = {10.1109/IJCNN.2013.6706973},
author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and M. J. del Jesus and Francisco Mart{\'\i}nez}
}
@conference {6007647,
title = {Using the Adaboost algorithm for extracting fuzzy rules from low quality data: Some preliminary results},
booktitle = {2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)},
year = {2011},
month = {June},
pages = {1263-1270},
abstract = {When the Adaboost algorithm is used for extracting fuzzy rules from data, each rule is regarded as a weak learner, and knowledge bases as assimilated to ensembles. In this paper we propose an extension of this framework for obtaining fuzzy rule-based classifiers from imprecise data. In the new approach, the mentioned search of the best rule at each iteration is carried out by a genetic algorithm with a fuzzy fitness function. The instances will be assigned fuzzy weights, however each fuzzy rule will be associated to a crisp number of votes.},
keywords = {Adaboost algorithm, Boosting, data handling, Electronic mail, fuzzy fitness function, fuzzy rule extraction, fuzzy rule-based classifiers, fuzzy set theory, Fuzzy systems, fuzzy weights, genetic algorithm, genetic algorithms, Genetic Fuzzy Systems, knowledge based systems, knowledge bases, learning (artificial intelligence), Low Quality Data, Merging, Optimization, pattern classification, Training},
issn = {1098-7584},
doi = {10.1109/FUZZY.2011.6007647},
author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
@conference {5454152,
title = {Introducing a genetic fuzzy linguistic combination method for bagging fuzzy rule-based multiclassification systems},
booktitle = {2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)},
year = {2010},
month = {March},
pages = {75-80},
abstract = {Many different fuzzy aggregation operators have been successfully used to combine the outputs provided by the individual classifiers in a multiclassification system. However, up to our knowledge, the use of fuzzy combination methods composed of a fuzzy system is less extended. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would show a hierarchical structure and the operation of the latter component would be transparent to the user. Moreover, for the specific case of fuzzy multiclassification systems, the new approach could also become a smart way to allow fuzzy classifiers to deal with high dimensional problems avoiding the curse of dimensionality. The present contribution establishes the first basis in this direction by introducing a genetic fuzzy system-based framework to build the fuzzy linguistic combination method for a bagging fuzzy multiclassification system.},
keywords = {Bagging, bagging fuzzy multiclassification system, Classification tree analysis, classifier ensemble, Computer science, Decision trees, fuzzy aggregation operators, fuzzy linguistic combination methods, fuzzy linguistic rule-based system, Fuzzy reasoning, fuzzy set theory, Fuzzy systems, genetic algorithms, genetic fuzzy-based system, Genetics, knowledge based systems, learning (artificial intelligence), machine learning, Neural networks, pattern classification},
doi = {10.1109/GEFS.2010.5454152},
author = {L. S{\'a}nchez and O. Cord{\'o}n and A. Quirin and K. Trawinski}
}
@conference {4484560,
title = {A first study on bagging fuzzy rule-based classification systems with multicriteria genetic selection of the component classifiers},
booktitle = {2008 3rd International Workshop on Genetic and Evolving Systems},
year = {2008},
month = {March},
pages = {11-16},
keywords = {Bagging, bagging fuzzy rule-based classification system, Boosting, component classifier, Design methodology, evolutionary computation, fuzzy set theory, Fuzzy systems, genetic algorithms, heuristic fuzzy classification rule generation method, Humans, knowledge based systems, learning (artificial intelligence), machine learning, multicriteria genetic algorithm, pattern classification, Proposals, Scalability},
doi = {10.1109/GEFS.2008.4484560},
author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez}
}
@article {4286977,
title = {Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems},
journal = {IEEE Transactions on Fuzzy Systems},
volume = {15},
number = {4},
year = {2007},
month = {Aug},
pages = {551-562},
abstract = {In our opinion, and in accordance with current literature, the precise contribution of genetic fuzzy systems to the corpus of the machine learning theory has not been clearly stated yet. In particular, we question the existence of a set of problems for which the use of fuzzy rules, in combination with genetic algorithms, produces more robust models, or classifiers that are inherently better than those arising from the Bayesian point of view. We will show that this set of problems actually exists, and comprises interval and fuzzy valued datasets, but it is not being exploited. Current genetic fuzzy classifiers deal with crisp classification problems, where the role of fuzzy sets is reduced to give a parametric definition of a set of discriminant functions, with a convenient linguistic interpretation. Provided that the customary use of fuzzy sets in statistics is vague data, we propose to test genetic fuzzy classifiers over imprecisely measured data and design experiments well suited to these problems. The same can be said about genetic fuzzy models: the use of a scalar fitness function assumes crisp data, where fuzzy models, a priori, do not have advantages over statistical regression.},
keywords = {Bayesian methods, data handling, Design for experiments, discriminant function, fuzzy fitness function, fuzzy rule-based classifiers, fuzzy rule-based models, fuzzy rules, fuzzy set theory, Fuzzy sets, Fuzzy systems, genetic algorithm, genetic algorithms, genetic fuzzy classifiers, Genetic Fuzzy Systems, learning (artificial intelligence), linguistic interpretation, machine learning, machine learning theory, Parametric statistics, pattern classification, Robustness, scalar fitness function, statistical analysis, Stochastic resonance, vague data},
issn = {1063-6706},
doi = {10.1109/TFUZZ.2007.895942},
author = {L. S{\'a}nchez and I. Couso}
}
@conference {4295659,
title = {Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms},
booktitle = {2007 IEEE International Fuzzy Systems Conference},
year = {2007},
month = {July},
pages = {1-6},
abstract = {Incremental rule base learning techniques can be used to learn models and classifiers from interval or fuzzy-valued data. These algorithms are efficient when the observation error is small. This paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality of the iterative learning degrades in this kind of problems, and that it does not make full use of all the available information. As an alternative, we propose a new implementation of a mutiobjective Michigan-like algorithm, where each individual in the population codifies one rule and the individuals in the Pareto front form the knowledge base.},
keywords = {Degradation, Fuzzy sets, Fuzzy systems, genetic algorithms, Global Positioning System, incremental rule base learning techniques, Iterative algorithms, iterative learning degrades, knowledge based systems, learning (artificial intelligence), learning fuzzy linguistic models, Low Quality Data, Noise measurement, Pareto front form, Pareto optimisation, Position measurement, Stochastic resonance, Uncertainty},
issn = {1098-7584},
doi = {10.1109/FUZZY.2007.4295659},
author = {L. S{\'a}nchez and J. Otero}
}
@conference {4016720,
title = {A Multiobjective Genetic Fuzzy System with Imprecise Probability Fitness for Vague Data},
booktitle = {2006 International Symposium on Evolving Fuzzy Systems},
year = {2006},
month = {Sep.},
pages = {131-136},
abstract = {When questionnaires are designed, each factor under study can be assigned a set of different items. The answers to these questions must be merged in order to obtain the level of that input. Therefore, it is typical for data acquired from questionnaires that each of the inputs and outputs are not numbers, but sets of values. In this paper, we represent the information contained in such a set of values by means of a fuzzy number. A fuzzy statistics-based interpretation of the semantic of a fuzzy set is used for this purpose, as we consider that this fuzzy number is a nested family of confidence intervals for the value of the variable. The accuracy of the model is expressed by means of an interval-valued function, derived from a definition of the variance of a fuzzy random variable. A multicriteria genetic learning algorithm, able to optimize this interval-valued function, is proposed. As an example of the application of this algorithm, a practical problem of modeling in marketing is solved},
keywords = {Computer errors, Computer science, Consumer behavior, fuzzy random variable variance, fuzzy set, fuzzy set theory, Fuzzy sets, fuzzy statistics-based interpretation, Fuzzy systems, genetic algorithms, Genetics, imprecise probability fitness, interval-valued function, learning (artificial intelligence), multicriteria genetic learning algorithm, multiobjective genetic fuzzy system, probability, Random variables, Statistics, vague data},
doi = {10.1109/ISEFS.2006.251156},
author = {L. S{\'a}nchez and I. Couso and J. Casillas}
}
@conference {1681710,
title = {Knowledge Extraction from Fuzzy Data for Estimating Consumer Behavior Models},
booktitle = {2006 IEEE International Conference on Fuzzy Systems},
year = {2006},
month = {July},
pages = {164-170},
abstract = {For certain problems of casual modeling in marketing, the information is obtained by means of questionnaires. When these questionnaires include more than one item for each observable variable, the value of this variable can not be assigned a number, but a potentially scattered set of values. In this paper, we propose to represent the information contained in this set of values by means of a fuzzy number. A novel fuzzy statistics-based interpretation of the semantic of a fuzzy set will be used for this purpose, as we will consider that this fuzzy number is a nested family of confidence intervals for a central tendency measure of the value of the variable. A genetic learning algorithm, able to extract association fuzzy rules from this data, is also proposed. The accuracy of the model will be expressed by means of a fuzzy-valued function. We propose to jointly minimize this function and the complexity of the rule based model with multicriteria genetic algorithms, that in turn will depend on a fuzzy ranking-based ordering of individuals.},
keywords = {Artificial intelligence, association fuzzy rule extraction, casual modeling, Computer science, Computer science education, Consumer behavior, consumer behavior model, consumer behaviour, data mining, fuzzy data, fuzzy logic, fuzzy number, fuzzy ranking, fuzzy set theory, Fuzzy sets, fuzzy statistics, genetic algorithms, knowledge extraction, learning (artificial intelligence), learning algorithm, marketing data processing, multicriteria genetic algorithm, Scattering, semantic interpretation, statistical analysis},
issn = {1098-7584},
doi = {10.1109/FUZZY.2006.1681710},
author = {J. Casillas and L. S{\'a}nchez}
}
@conference {943781,
title = {A fast genetic method for inducting linguistically understandable fuzzy models},
booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)},
volume = {3},
year = {2001},
month = {July},
pages = {1559-1563 vol.3},
abstract = {Fuzzy rule bases can be regarded as mixtures of experts, and boosting techniques can be applied to learn them from data. In particular, provided that adequate reasoning methods are used, fuzzy models are extended additive models, thus backfitting can be applied to them. We propose to use an implementation of backfitting that uses a genetic algorithm for fitting submodels to residuals and we also show that it is both more accurate and faster than other fuzzy rule learning methods.},
keywords = {Artificial intelligence, backfitting, boosting techniques, computational linguistics, extended additive models, fast genetic method, fuzzy logic, fuzzy models, Fuzzy reasoning, fuzzy rule bases, fuzzy rule learning methods, fuzzy set theory, Fuzzy sets, genetic algorithm, genetic algorithms, inference mechanisms, learning (artificial intelligence), Learning systems, linguistically understandable fuzzy model induction, mixtures of experts, reasoning methods, residuals, submodel fitting, Training data, uncertainty handling},
doi = {10.1109/NAFIPS.2001.943781},
author = {L. S{\'a}nchez}
}