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| Motohisa Funabashi, Akira Maeda, Yasuo Morooka, Kiyomi Mori, "Fuzzy and Neural Hybrid Expert Systems: Synergetic AI," IEEE Intelligent Systems, vol. 10, no. 4, pp. 32-40, August, 1995. | |||
| BibTex | x | ||
| @article{ 10.1109/64.403949, author = {Motohisa Funabashi and Akira Maeda and Yasuo Morooka and Kiyomi Mori}, title = {Fuzzy and Neural Hybrid Expert Systems: Synergetic AI}, journal ={IEEE Intelligent Systems}, volume = {10}, number = {4}, issn = {0885-9000}, year = {1995}, pages = {32-40}, doi = {http://doi.ieeecomputersociety.org/10.1109/64.403949}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - IEEE Intelligent Systems TI - Fuzzy and Neural Hybrid Expert Systems: Synergetic AI IS - 4 SN - 0885-9000 SP32 EP40 EPD - 32-40 A1 - Motohisa Funabashi, A1 - Akira Maeda, A1 - Yasuo Morooka, A1 - Kiyomi Mori, PY - 1995 VL - 10 JA - IEEE Intelligent Systems ER - | |||
After a brief review of the foundations and history of fuzzy systems technology, the authors describe two applications from the next generation.
Lotfi Zadeh introduced the concept of fuzzy sets in 1965. In 1974, E.H. Mamdani invented a fuzzy inference procedure, thus setting the stage for the initial development and proliferation of fuzzy system applications. Logic programming also played an important role in disseminating the idea of fuzzy inference, as it emphasized the importance of non-numerical knowledge over traditional mathematical models.
The most recent wave of fuzzy expert system technology uses consolidated hybrid architectures, what we call Synergetic AI. These architectures developed in response to the limitations of previous large-scale fuzzy expert systems. (Please see the sidebars on Mamdani's fuzzy inference procedure and the history of fuzzy system applications) After a brief introduction to hybrid architecture, we describe two types: combination and fusion. Thus far the research in this area has not focused on practical experience. We discuss the development and implementation of a combination architecture fuzzy system for a steel-making plant. We also consider the significant algorithmic strength that fusion architecture lends fuzzy systems.