Aggregation of Evidence in a Fuzzy Knowledge-Based Method for Automated Tuning of Microwave Electric Circuits

Article type: Research Article

Authors: Ukita, Akio | Karwowski, Waldemar | Salvendy, Gavriel

Affiliations: Production Engineering Development Laboratory, NEC Co. Tukagoshi, Saiwaiku, Kawasaki 210, Japan | Center for Industrial Ergonomics, Department of Industrial Engineering, University of Louisville, Louisville, Kentucky 40292 | School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907

Abstract: The electric circuit tuning process, which requires manual tuning of a set of trimmers by the human operators, was automated through the application of a fuzzy knowledge-based system. In a complex tuning process, multiple circuit specification criteria had to be simultaneously satisfied by several trimmers. The main objective of this study was to examine different tuning evidence aggregation methods in order to reduce the overall circuit tuning time. In the proposed fuzzy knowledge-based system, the effect of each trimmer on each tuning criterion was expressed by a grade of the fuzzy membership related to each circuit output. The overall effect of each trimmer on the circuit tuning performance was modeled by an aggregation of the grades used for trimmer selection. The model simulation results showed that the geometrical average operator was the best method for evidence aggregation. To compensate for the lack of fuzzy rules, some heuristic rules were also introduced to adjust the aggregated evidence values. It was shown that these rules significantly improved the performance of the tuning system. Finally, it was observed that although the tuning rules were elicited from human experts, it was not essential in this study to emulate the human's information aggregation processes. This was due to the fact that in the manual tuning process the human aggregation of evidence about circuit performance did not necessarily provide the best solution for the intended task.

DOI: 10.3233/IFS-1994-2403

Journal: Journal of Intelligent and Fuzzy Systems, vol. 2, no. 4, pp. 299-313, 1994