Adaptive learning of an evolving cascade neo-fuzzy system in data stream mining tasks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Control and Optimization,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering
Link
http://link.springer.com/content/pdf/10.1007/s12530-016-9149-5.pdf
Reference52 articles.
1. Aggarwal C (2007) Data streams: models and algorithms (advances in database systems). Springer, New York
2. Angelov P (2013) Autonomous learning systems: from data streams to knowledge in real-time. Wiley, Chichester
3. Angelov P, Filev D (2004) An approach to online identification of takagi-sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B Cybern 34(1):484–498
4. Angelov P, Lughofer E (2008) Data-driven evolving fuzzy systems using ets and flexfis: comparative analysis. Int J Gen Syst 37(1):45–67
5. Angelov P, Zhou X (2006) Evolving fuzzy systems from data streams in real-time. In: 2006 international symposium on evolving fuzzy systems, pp 29–35
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