@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 {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}
}
