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: A method for synthetic opinions to yield a robust fuzzy expert system
1Department of Aerospace Engineering, M S Ramaiah University of Applied Sciences, 470-P, Peenya 4th Phase, Peenya Industrial Area, Bengaluru, Karnataka 560058, India.
Methodsx
|March 27, 2023
Summary
A new Method for SYnthetic Opinions (MYSO) generates synthetic expert opinions to create robust Fuzzy Expert Systems (FES) when human experts are scarce. MYSO demonstrated strong correlations with human expert opinions in diverse applications.
Area of Science:
- Artificial Intelligence
- Fuzzy Logic Systems
- Expert Systems
Background:
- Decision-making in many fields is hindered by the limited availability of human experts.
- Insufficient expert opinions can compromise the robustness of resulting solutions.
- Fuzzy Expert Systems (FES) require expert input for reliable decision-making.
Purpose of the Study:
- To introduce a novel method, the Method for SYnthetic Opinions (MYSO), for generating synthetic expert opinions.
- To enhance the robustness of Fuzzy Expert Systems (FES) by simulating expert consensus.
- To validate the efficacy of MYSO in producing reliable synthetic opinions.
Main Methods:
- MYSO generates opinions from "synthetic experts" using a normal distribution.
- FES opinions are derived from antecedent vectors sampled from a uniform distribution.
- Weights of fuzzy rules are optimized to align synthetic and human expert opinions.
Main Results:
- MYSO-generated opinions showed high correlations with human expert opinions in an Industrial Development Project (IDP) (91.4%-98.0%).
- Correlations for passenger car performance (PCP) ranged from 85.6% to 90.8%.
- The method successfully produced robust FES even with limited human expert data.
Conclusions:
- MYSO effectively generates synthetic expert opinions for robust FES development.
- The method is validated across distinct domains, confirming its applicability.
- MYSO offers a viable solution for expert system development when human expertise is limited.
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