The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction
Open accessAbstract
This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.
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Cites
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Intelligent agents: theory and practice
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A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)
Albus, J. S. (1975) · Journal of Dynamic Systems, Measurement, and Control · doi:10.1115/1.3426922 · Cited by 2,177 · cited 2×
“DENFIS uses a local generalization, like EFuNN and CMAC neural networks [1], so it needs more training data than the models which use global generalization such as ANFIS and MLP.”
Cited by
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
Ang, K. K., Chin, Z. Y., Zhang, H., & Guan, C. (2008) · 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) · doi:10.1109/ijcnn.2008.4634130 · Cited by 1,514
Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification
Feng, S., & Chen, C. L. P. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2857815 · Cited by 490
A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification
Deng, Y., Ren, Z., Kong, Y., Bao, F., & Dai, Q. (2017) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2016.2574915 · Cited by 396
SOFMLS: Online Self-Organizing Fuzzy Modified Least-Squares Network
de Jesus Rubio, J. (2009) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2009.2029569 · Cited by 346
On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers
Wu, D. (2012) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2012.2186818 · Cited by 339
Metadata
- Authors
- Kasabov, N. K., & Song, Q.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 2002-04
- Volume
- 10
- Issue
- 2
- Pages
- 144-154
- ISSN
- 1063-6706 (ISSN-L)
- OpenAlex
- W2162635690
- Cited by
- 1,264
- References
- 4
- FWCI
- 26.10
- Type
- journal article
- Language
- en
- Open access
- green
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