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Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants.
(Abiyev, Rahib H.,) |
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Bibliographical information (record 263985) |
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- In industry, most dynamical plants are characterized by unpredictable and hard-to-formulate factors, uncertainty, and fuzziness of information, and as a result, deterministic models usually prove to be insufficient to adequately describe the process. In such situations, the use of fuzzy approaches becomes a viable alternative. However, the systems constructed on the base of type 1 fuzzy systems cannot directly handle the uncertainties associated with information or data in the knowledge base of the process. One possible way to alleviate the problem is to resort to the use of type 2 fuzzy systems. In this paper, the structure of a type 2 Takagi–Sugeno–Kang fuzzy neural system is presented, and its parameter update rule is derived based on fuzzy clustering and gradient learning algorithm. Its performance for identification and control of time-varying as well as some time-invariant plants is evaluated and compared with other approaches seen in the literature. It is seen that the proposed structure is a potential candidate for identification and control purposes of uncertain plants, with the uncertainties being handled adequately by type 2 fuzzy sets. [ABSTRACT FROM PUBLISHER] $b Copyright of IEEE Transactions on Industrial Electronics is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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EOL-12
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Item available
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NEU Grand LibraryOnline (QA76.87 .T97)
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Online electronic |
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