Aggarwal CC, Yu PS (2008) A framework for clustering uncertain data streams. IEEE international conference on data engineering, pp 150–159

Angelov P, Filev D (2004) An approach to online identification of Takagi–Sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B 34(1):484–498

CrossRefAngelov P, Zhou X (2006) Evolving fuzzy systems from data streams in real-time. International symposium on evolving fuzzy systems, pp 29–35

Angelov P, Zhou X (2008) Evolving fuzzy-rule-based classifiers from data streams. IEEE Trans Fuzzy Syst 16(6):1462–1475

CrossRefAstrom KJ, Wittenmark B (1994) Adaptive control, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston

Bargiela A, Pedrycz W (2002) Granular computing: an introduction, 1st edn. Kluwer Academic, Dordrecht

Bargiela A, Pedrycz W (2005) Granular mappings. IEEE Trans Syst Man Cybern Part A 35(2):292–297

CrossRefBargiela A, Pedrycz W (2008) Toward a theory of granular computing for human-centered information processing. IEEE Trans Fuzzy Syst 16(2):320-330

CrossRefBeringer J, Hullermeier E (2007) Efficient instance-based learning on data streams. Intell Data Anal 11(6):627–650

Bouchachia A (2010) An evolving classification cascade with self-learning. Evol Syst 1(3):143–160

CrossRefBox GEP, Jenkins GM, Reinsel GC (2008) Time series analysis: forecasting and control, 4th edn. Wiley Series in Probability and Statistics, New York

Dubois D, Prade H (2004) On the use of aggregation operations in information fusion processes. Fuzzy Sets Syst 142(1):143–161

MathSciNetMATHCrossRefGabrys B, Bargiela A (2000) General fuzzy min–max neural network for clustering and classification. IEEE Trans Neural Netw 11(3):769–783

CrossRefHaykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood Cliffs

Johnson CR (1988) Lectures on adaptive parameter estimation. Prentice-Hall, Inc., Upper Saddle River

Kasabov N (2007) Evolving connectionist systems: the knowledge engineering approach, 2nd edn. Springer, Berlin

Kasabov N, Song Q (2002) DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Trans Fuzzy Syst 10(2):144–154

CrossRefLeite D, Gomide F (2012) Evolving linguistic fuzzy models from data streams. In: Trillas E, Bonissone P, Magdalena L, Kacprycz J (eds) Studies in fuzziness and soft computing: a homage to Abe Mamdani. Springer, Berlin, pp 209–223

Leite D, Costa P, Gomide F (2010a) Evolving granular neural network for semi-supervised data stream classification. Int Joint Conf Neural Netw, pp 1–8

Leite D, Costa P, Gomide F (2010b) Granular approach for evolving system modeling. In: Hullermeier E, Kruse R, Hoffmann F (eds) Lecture notes in artificial intelligence, vol 6178. Springer, Berlin, pp 340–349

Leite D, Costa P, Gomide F (2012a) Interval approach for evolving granular system modeling. In: Mouchaweh MS, Lughofer E (eds) Learning in non-stationary environments: methods and applications. Springer, Berlin

Leite D, Costa P, Gomide F (2012b) Evolving granular neural networks from fuzzy data streams. Neural Netwo (Submitted)

Leite D, Gomide F, Ballini R, Costa P (2011) Fuzzy granular evolving modeling for time series prediction. IEEE international conference on fuzzy systems, pp 2794–2801

Lemos A, Caminhas W, Gomide F (2011) Fuzzy evolving linear regression trees. Evol Syst 2(1):1–14

CrossRefLin TY (2002) Neural networks, qualitative fuzzy logic and granular adaptive systems. World Congress of Computational Intelligence, pp 566–571

Lughofer E, Angelov P (2011) Handling drifts and shifts in on-line data streams with evolving fuzzy systems. Appl Soft Comput 11(2):2057–2068

CrossRefLughofer E, Bouchot J-L, Shaker A (2011) On-line elimination of local redundancies in evolving fuzzy systems. Evol Syst 2(3):165–187

CrossRefNandedkar AV, Biswas PK (2009) A granular reflex fuzzy min–max neural network for classification. IEEE Trans Neural Netw 20(7):1117–1134

CrossRefPedrycz W (2007) Granular computing—the emerging paradigm. J Uncertain Syst 1:38–61

Pedrycz W (2010) Evolvable fuzzy systems: some insights and challenges. Evol Syst 1(2):73–82

MathSciNetCrossRefPedrycz W, Gomide F (2007) Fuzzy systems engineering: toward human-centric computing. Wiley-Hoboken, NJ

Pedrycz W, Skowron A, Kreinovich V (eds) (2008) Handbook of granular computing, Wiley-Interscience, New York

Roof S, Callagan C (2003) The climate of Death Valley, California. Bull Am Meteorol Soc 84:1725–1739

CrossRefRubio JJ (2010) Stability analysis for an online evolving neuro-fuzzy recurrent network. In: Angelov P, Filev D, Kasabov N (eds) Evolving intelligent systems: methodology and applications, Wiley/IEEE Press, New York, pp 173–199

Simpson PK (1992) Fuzzy min–max neural networks. Part I: classification. IEEE Trans Neural Netw 3(5):776–786

CrossRefSimpson PK (1993) Fuzzy min–max neural networks. Part II: clustering. IEEE Trans Fuzzy Syst 1(1):32–45

CrossRefYager RR (2007) Learning from imprecise granular data using trapezoidal fuzzy set representations. In: Prade H, Subrahmanian VS (eds) Lecture notes in computer science. Springer, Berlin, vol 4772, pp 244–254

Yager RR (2009) Participatory learning with granular observations. IEEE Trans Fuzzy Syst 17(1):1–13

CrossRefYao JT (2007) A ten-year review of granular computing. IEEE international conference on granular computing, pp 734–739

Yao YY (2008) Granular computing: past, present and future. IEEE international conference on granular computing, pp 80–85

Yao YY (2010) Human-inspired granular computing. In: Yao JT (ed) Novel developments in granular computing: applications for advanced human reasoning and soft computation

Young PC (1984) Recursive estimation and time-series analysis: an introduction. Springer, Berlin

Zadeh LA (1979) Fuzzy sets and information granularity, In: Gupta MM, Ragade RK, Yager RR (eds) Advances in fuzzy set theory and applications, North Holland, Amsterdam, pp 3–18