Algorithm for Tuning Fuzzy Inference in Medical Information Systems Based on Knowledge
The purpose of research is to improve the tuning of fuzzy inference in medical information systems based on knowledge. Methods. The leading approach to the study of this problem is the adjustment of the rules of fuzzy inference systems based on the principles of the proportional-integral controller. Results. The relevance of the study is due to the need to improve medical information systems by introducing artificial intelligence technologies. The key element of such systems is the decision inference mechanism, which implements logical inference from a pre-built base of facts and rules in accordance with the laws of formal logic. The efficiency of the decision inference mechanism essentially depends on the quality of its configuration. The article presents an algorithm for tuning Sugeno-type fuzzy inference systems, which allows adjusting the rules of fuzzy inference systems with minimizing the adjustment time and overshoot time, as well as the results of studying the dependence of the error and overshoot on the values of the integral and proportional components. Conclusion. Development of an algorithm for tuning fuzzy logical inference in medical information systems based on knowledge, which minimizes the time of tuning decision rules and the time of their overshoot. The materials of the article are of practical value for improving knowledge-based information systems. The priorities for further research are the study of the dependence of the rate of adjustment of the decision rules on the form of the membership functions of the input variables and the value of the limiting factor of the integral component.