The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Neural networks that learn from fuzzy if-then rules
Abstract
An architecture for neural networks that can handle fuzzy input vectors is proposed, and learning algorithms that utilize fuzzy if-then rules as well as numerical data in neural network learning for classification problems and for fuzzy control problems are derived. The learning algorithms can be viewed as an extension of the backpropagation algorithm to the case of fuzzy input vectors and fuzzy target outputs. Using the proposed methods, linguistic knowledge from human experts represented by fuzzy if-then rules and numerical data from measuring instruments can be integrated into a single information processing system (classification system or fuzzy control system). It is shown that the scheme works well for simple examples.>
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Cites
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Cited by
Monitoring system for photovoltaic plants: A review
Madeti, S. R., & Singh, S. N. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.09.088 · Cited by 313
Kuo, R. J., Chen, C. H., & Hwang, Y. C. (2001) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(98)00399-6 · Cited by 306
(1997) · Elsevier eBooks · doi:10.1016/b978-0-12-526430-3.x5000-4 · Cited by 290
Metadata
- Authors
- Ishibuchi, H., Fujioka, R., & Tanaka, H.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 1993-05
- Volume
- 1
- Issue
- 2
- Pages
- 85-97
- ISSN
- 1063-6706 (ISSN-L)
- OpenAlex
- W2144534900
- Cited by
- 287
- References
- 30
- FWCI
- 18.76
- Type
- journal article
- Language
- en
- Open access
- closed
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