Evolving fuzzy neural networks for supervised/unsupervised online knowledge-based learning

By Nikola K. Kasabov. IEEE Trans. Syst. Man Cybern. Part B, 2001

501 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/44525315e289b4c26e47e789a423fa68aa12c3ec

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In Artificial Intelligence

Citations in the corpus

SOFMLS: Online Self-Organizing Fuzzy Modified Least-Squares Network (2009, methodology): “005 ) [31] is shown in Table I, where the root means square error (RMSE) [23] is From Table I, it can be seen that the SOFMLS achieves better accuracy when compared with the other networks.”
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach (2006, unknown): “4) Evolving fuzzy neural networks (EFuNNs) [59].”
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift (2010, background): “(2001), as it is more clear than the general notions given by authors such as Littlestone (1988) and is very similar to the definitions given by Schaal and Atkeson (1998) and Kasabov (2003). According to Polikar et al.”
Predictive functional control based on an adaptive fuzzy model of a hybrid semi-batch reactor (2010, methodology): “There are many methods that deal with on-line fuzzy model identification, for example, the evolving fuzzy neural network (EFuNN) (Kasabov, 2001), the dynamic evolving neural-fuzzy Contents lists available at ScienceDirect”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, unknown): “learning and unsupervised learning [53].”
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models (2008, methodology): “In this sense, in order to cope with the online demands mentioned before, various approaches for the so-called evolving fuzzy and neurofuzzy systems were developed in the last decade, such as eTS [9] and its modified version Simp_eTS [10] for rule base simplification, the dynamic evolving neural-fuz”
Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach (2010, unknown): “The usefulness of existing neural-fuzzy systems such as ANFIS (Jang, 1993), POPFNN (Ang & Quek, 2005; Quek & Zhou, 1996), HyFIS (Kim & Kasabov, 1999), EFuNN (Kasabov, 2001), SOFNNGA (Leng, McGinnity, & Prasad, 2006), RPOP-TVR (Wong, Cho, & Quek, 2009), FASCOM (Goh, Lim, & Quek, 2009) and FITSK (Quah”