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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tung, S.W. Quek, C. Guan, C. |
| Copyright Year | 2009 |
| Description | Author affiliation: Center for Computational Intelligence, Block N4 #2A-32, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, Singapore 639798 (Quek, C.) || Institute for Infocomm Research, A¿Star, Singapore (Guan, C.) || Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore (Tung, S.W.) |
| Abstract | The Hybrid neural Fuzzy Inference System (Hy-FIS) is a five layers adaptive neural fuzzy inference system, based on the Compositional Rule of Inference (CRI) scheme, for building and optimizing fuzzy models. To provide the HyFIS architecture with a firmer and more intuitive logical framework that emulates the human reasoning and decision-making mechanism, the fuzzy Yager inference scheme, together with the self-organizing gaussian Discrete Incremental Clustering (gDIC) technique, were integrated into the HyFIS network to produce the HyFIS-Yager-gDIC . This paper presents T2-HyFIS-Yager, a Type-2 Hybrid neural Fuzzy Inference System realizing Yager inference, for learning and reasoning with noise corrupted data. The proposed T2-HyFIS-Yager is used to perform time-series forecasting where a non-stationary time-series is corrupted by additive white noise of known and unknown SNR to demonstrate its superiority as an effective neuro-fuzzy modeling technique. |
| Starting Page | 80 |
| Ending Page | 85 |
| File Size | 540280 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424435968 |
| ISSN | 10987584 |
| DOI | 10.1109/FUZZY.2009.5277345 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-20 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fuzzy systems Fuzzy reasoning Buildings Humans Decision making Fuzzy logic Fuzzy neural networks Additive white noise Signal to noise ratio Predictive models |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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