Abstract
A concept of an automatic knowledge base tuning in complex systems is outlined. In real life systems although the system is successfully controlled by a human expert, some information may be lost in translating the expert's knowledge to linguistic rules. On the other hand, the information gathered by sensor measurements from past experiences is not enough for a successful design, because the past performances, in general, would not cover all the possible situations we may encounter in future operations. We focus on tuning the already existing knowledge data base, as that should provide us with a successful control of complex processes. An application to the Wright Patterson Air Force Base incendiary projectile data is presented to corroborate the theory.
This work was supported in part by grant IRI940003P from Pittsburgh Supercomputer Center through National Science Foundation.
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References
C. C. Lee, Fuzzy Logic in Control Systems: Fuzzy Logic Controller — Parts I and II, IEEE Trans. Syst. Man Cybern. (20) 2, (1990) 404–435.
T. Ohtani, M. Negishi, and J. Murakami, Fuzzy Control of Basis Weight Profile for Paper Machines, Yokogawa Technical Report, English Edition 11, (1990) 52–58.
S. Shao, Fuzzy Self-Organizing Controller and its Application for Dynamic Processes, Fuzzy Sets and Systems 26, (1988) 151–164.
D. G. Burkhardt and P. P. Bonissone, Automated Fuzzy Knowledge Base Generation and Tuning, in: Proceedings of the 1st IEEE International Conference on Fuzzy Systems, (San Diego, 1992) 179–188.
L. M. Sztandera, Error Propagation Fuzzy Control System, Information Sciences 3 (2), (1995) 75–89.
L. M. Sztandera, Experience Augmented Linguistic Model for Real Industrial System, Advances in Modeling and Simulation 19 (3), (1990) 55–63.
H. Ishibuchi, R. Fujioka, and H. Tanaka, Neural Networks that Learn from Fuzzy If-Then Rules, IEEE Transactions on Fuzzy Systems 1 (2), (1993) 85–97.
B. Kosko, Neural Networks and Fuzzy Systems, (Prentice Hall, Englewood Cliffs, 1992).
L. M. Sztandera and K. J. Cios, Incendiary Projectile Classifier Using Fuzzy Set Theory, in: Proceedings of the North American Fuzzy Information Processing Society, NAFIPS'93, (Allentown, 1993) 103–107.
L. X. Wang and J. M. Mendel, Generating Fuzzy Rules by Learning from Examples, IEEE Transactions on Systems, Man, and Cybernetics 22 (6), (1992) 1414–1427.
M. Sugeno, An Introductory Survey of Fuzzy Control, Information Science 36, (1985) 59–83.
L. M. Sztandera, Experience Augmented Expert System in Process Automation, in: Proceedings of International Conference “Control and Optimization of Transport and Industrial Processes”, (Zakopane, 1988) 381–390.
L. M. Sztandera, Expert Controller in the X-ray Industrial System, in: Proceedings of 3rd International Workshop on Process Automation, Vol. 3, (Wroclaw, 1988) 73–76.
L. M. Sztandera, Aspects of Microprocessor Control in the X-ray Industrial System, in: Proceedings of 3rd International Conference on Signal Transformation, (Bydgoszcz, 1988) 289–294.
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© 1997 Springer-Verlag Berlin Heidelberg
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Sztandera, L.M. (1997). Automatic knowledge base tuning. In: Martin, T.P., Ralescu, A.L. (eds) Fuzzy Logic in Artificial Intelligence Towards Intelligent Systems. FLAI 1995. Lecture Notes in Computer Science, vol 1188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62474-0_9
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DOI: https://doi.org/10.1007/3-540-62474-0_9
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