@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 {6622418,
title = {CI-LQD: A software tool for modeling and decision making with Low Quality Data},
booktitle = {2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
year = {2013},
month = {July},
pages = {1-8},
abstract = {The software tool CI-LQD (Computational Intelligence for Low Quality Data) is introduced in this paper. CI-LQD is an ongoing project that includes a lightweight open source software that has been designed with scientific and teaching purposes in mind. The main usefulness of the software is to automate the calculations involved in the statistical comparisons of different algorithms, with both numerical and graphical techniques, when the available information is interval-valued, fuzzy, incomplete or otherwise vague. A growing catalog of evolutionary algorithms for learning classifiers, models and association rules, along with their corresponding data conditioning and preprocessing techniques, is included. A demonstrative example of the tool is described that illustrates the capabilities of the software.},
keywords = {Algorithm design and analysis, Artificial intelligence, Association rules, CI-LQD, Computational intelligence, data conditioning, data mining, data preprocessing technique, Databases, Decision making, evolutionary algorithm, Evolutionary algorithms, evolutionary computation, graphical technique, learning classifier, lightweight open source software, Low Quality Data, Probability distribution, Software algorithms, software tool, software tools, statistical analysis, statistical comparison},
issn = {1098-7584},
doi = {10.1109/FUZZ-IEEE.2013.6622418},
author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
@conference {6622420,
title = {Engine Health Monitoring for engine fleets using fuzzy radviz},
booktitle = {2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
year = {2013},
month = {July},
pages = {1-8},
abstract = {A new algorithm for assessment of Engine Health Monitoring (EHM) data in aircraft is proposed. The diagnostic tool quantifies step changes, shifts and trends in EHM data by means of a transformation that aggregates concurrent readings of EHM data into a single fuzzy state. A Genetic Fuzzy System is used to detect the occurance of a specific trend of interest in the sequence of states. The activation of the rules is represented in a 2D map by means of an extension of the Radviz visualization algorithm to fuzzy data.},
keywords = {2D map, aerospace engineering, aerospace engines, aircraft, Bandwidth, condition monitoring, data visualisation, diagnostic tool, EHM data, engine fleets, engine health monitoring, Engines, fault diagnosis, fuzzy data, fuzzy Radviz, fuzzy set theory, genetic algorithms, genetic fuzzy system, Genetic Fuzzy Systems, Low Quality Data, Maintenance engineering, Market research, mechanical engineering computing, Monitoring, Radviz visualization algorithm, rule activation, single fuzzy state, states sequence, Temperature measurement, Turbines},
issn = {1098-7584},
doi = {10.1109/FUZZ-IEEE.2013.6622420},
author = {A. Mart{\'\i}nez and L. S{\'a}nchez and I. Couso}
}
@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 {5584797,
title = {Preprocessing vague imbalanced datasets and its use in genetic fuzzy classifiers},
booktitle = {International Conference on Fuzzy Systems},
year = {2010},
month = {July},
pages = {1-8},
abstract = {When there is a substantial difference between the number of cases of the majority and minority classes, minimum error-based classification systems tend to overlook these last instances. This can be corrected either by preprocessing the dataset or by altering the objective function of the classifier. In this paper we analyze the first approach, in the context of genetic fuzzy systems (GFS), and in particular of those that can operate with imprecisely observed and low quality data. We will analyze the different preprocessing mechanisms of imbalanced datasets and will show the necessity of extending these for solving those problems where the data is both imprecise and im-balanced. In addition, we include a comprehensive description of a new algorithm, able to preprocess imprecise imbalanced datasets. Several real-world datasets are used to evaluate the proposal.},
keywords = {Classification algorithms, Context, data handling, Euclidean distance, fuzzy set theory, Fuzzy systems, genetic algorithms, genetic fuzzy classifier, genetic fuzzy system, Genetics, imbalanced dataset preprocessing, minimum error based classification system, Nearest neighbor searches, objective function, pattern classification, Pediatrics, Training},
issn = {1098-7584},
doi = {10.1109/FUZZY.2010.5584797},
author = {A. M. Palacios and L. S{\'a}nchez and I. Couso}
}
@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 {4222979,
title = {Modeling Vague Data with Genetic Fuzzy Systems under a Combination of Crisp and Imprecise Criteria},
booktitle = {2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making},
year = {2007},
month = {April},
pages = {30-37},
abstract = {Multicriteria genetic algorithms can produce fuzzy models with a good balance between their precision and their complexity. The accuracy of a model is usually measured by the mean squared error of its residual. When vague training data is used, the residual becomes a fuzzy number, and it is needed to optimize a combination of crisp and fuzzy objectives in order to learn balanced models. In this paper, we will extend the NSGA-II algorithm to this last case, and test it over a practical problem of causal modeling in marketing. Different setups of this algorithm are compared, and it is shown that the algorithm proposed here is able to improve the generalization properties of those models obtained from the defuzzified training data.},
keywords = {Additive noise, combination, Computer science, crisp objectives, defuzzified training data, fuzzy logic, fuzzy models, fuzzy objectives, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, Genetic Fuzzy Systems, Global Positioning System, mean squared error, multicriteria genetic algorithms, Noise measurement, NSGA-II algorithm, Position measurement, Probability distribution, Stochastic resonance, Training data, vague data modeling},
doi = {10.1109/MCDM.2007.369413},
author = {L. S{\'a}nchez and I. Couso and J. Casillas}
}
@conference {4295665,
title = {Some Results about Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data},
booktitle = {2007 IEEE International Fuzzy Systems Conference},
year = {2007},
month = {July},
pages = {1-6},
abstract = {Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied, partly because we lack numerical tools to quantify the interdependency between fuzzy random variables. In particular, many filter-type feature selection algorithms rely on crisp discretizations for estimating the mutual information between continuous variables, effectively preventing the use of vague data. Fuzzy rule based systems pass continuous input variables, in turn, through their own fuzzification interface. In the context of feature selection, should we rank the relevance of the inputs by means of their mutual information, it might happen that an apparently informative variable is useless after having been codified as a fuzzy subset of our catalog of linguistic terms. In this paper we propose to address both problems by estimating the mutual information with the same set of fuzzy partitions that will be used to codify the antecedents of the fuzzy rules. That is to say, we introduce a numerical algorithm for estimating the mutual information between two fuzzified continuous variables. This algorithm can be included in certain feature selection algorithms, and can also be used to obtain the most informative fuzzy partition for the data. The use of our definition will be exemplified with the help of some benchmark problems.},
keywords = {codification, computational linguistics, Data preprocessing, Feature extraction, feature selection, fuzzification interface, fuzzy discretization, fuzzy random variables, fuzzy rule based systems, fuzzy set theory, Fuzzy sets, Fuzzy systems, Information filtering, Information filters, knowledge based systems, linguistic terms, mutual information, Partitioning algorithms, Random variables, Spatial databases, vague data},
issn = {1098-7584},
doi = {10.1109/FUZZY.2007.4295665},
author = {L. S{\'a}nchez and M. R. Suarez and J. R. Villar and I. Couso}
}
@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}
}