title: Evolving granular neural networks from fuzzy data streams.
abstract: This paper introduces a granular neural network framework for evolving fuzzy system modeling from fuzzy data streams. The evolving granular neural network (eGNN) is able to handle gradual and abrupt parameter changes typical of nonstationary (online) environments. eGNN builds interpretable multi-sized local models using fuzzy neurons for information fusion. An online incremental learning algorithm develops the neural network structure from the information contained in data streams. We focus on trapezoidal fuzzy intervals and objects with trapezoidal membership function representation. More precisely, the framework considers triangular, interval, and numeric types of data to construct granular fuzzy models as particular arrangements of trapezoids. Application examples in classification and function approximation in material and biomedical engineering are used to evaluate and illustrate the neural network usefulness. Simulation results suggest that the eGNN fuzzy modeling approach can handle fuzzy data successfully and outperforms alternative state-of-the-art approaches in terms of accuracy, transparency and compactness.
authors.affiliation: Department of Computer Engineering and Automation, School of Electrical and Computer Engineering, University of Campinas-13083-852, Brazil. danfl7@dca.fee.unicamp.br
authors.forename: Daniel
authors.initials: D
authors.lastname: Leite
authors.forename: Pyramo
authors.initials: P
authors.lastname: Costa
authors.forename: Fernando
authors.initials: F
authors.lastname: Gomide
mesh_terms: D003198:Computer Simulation; D016208:Databases, Factual / Q000706:statistics & numerical data*; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D010300:Parkinson Disease / Q000175:diagnosis / Q000503:physiopathology