The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
NN-driven fuzzy reasoning
Open accessAbstract
Abstract A new fuzzy reasoning that can solve two problems of conventional fuzzy reasoning by combining an artificial neural network (NN) and fuzzy reasoning is proposed. These problems are (1) the lack of design for a membership function except a heuristic approach and (2) the lack of adaptability for possible changes in the reasoning environment. The proposed fuzzy reasoning approach solves these problems by using the learning function and nonlinearity of an NN. First, the problems involved in conventional fuzzy reasoning and the NN used in this paper are identified. Then a proposed algorithm is formulated and a concrete explanation using realistic data is developed. An example structure of an NN-driven fuzzy reasoning system is given, and two applications of this method are presented. This new fuzzy reasoning is capable of automatic determination of inference rules and adjustment according to the time-variant reasoning environment because of the use of NN in fuzzy reasoning. This proposed method can be applied to NN modeling and AI and is considered from the standpoint of the explicit incorporation of knowledge into the NN structure.
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Cites
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Cited by
ANFIS: adaptive-network-based fuzzy inference system
Jang, J.-S. R. (1993) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/21.256541 · Cited by 16,254
Fuzzy logic, neural networks, and soft computing
Zadeh, L. A. (1994) · Communications of the ACM · doi:10.1145/175247.175255 · Cited by 1,534
Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320
Multilayer perceptron, fuzzy sets, and classification
Pal, S. K., & Mitra, S. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.159058 · Cited by 1,113
Ten years of genetic fuzzy systems: current framework and new trends
Cordón, O., Gomide, F., Herrera, F., Hoffmann, F., & Magdalena, L. (2004) · Fuzzy Sets and Systems · doi:10.1016/s0165-0114(03)00111-8 · Cited by 819
Metadata
- Authors
- Takagi, H., & Hayashi, I.
- Venue
- International Journal of Approximate Reasoning
- Publisher
- Elsevier BV
- Published
- 1991-05
- Volume
- 5
- Issue
- 3
- Pages
- 191-212
- ISSN
- 0888-613X (ISSN-L)
- OpenAlex
- W2076579537
- Cited by
- 576
- References
- 4
- FWCI
- 20.50
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Takagi, H., & Hayashi, I. (1991). NN-driven fuzzy reasoning. International Journal of Approximate Reasoning. https://doi.org/10.1016/0888-613x(91)90008-a
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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