OPTIMIZED FUZZY RULE REDUCTION THROUGH KARNAUGH MAP-BASED SYSTEM

Control Engineering

In recent years, fuzzy logic systems have been employed in various applications with great success, controlling systems, machines, and consumer products. Although these systems are well-developed, issues persist regarding poor and unreliable performance quality. The number of design rules increases, resulting in a decrease in the accuracy of the controller, and the execution time of the system rises with the growing number of fuzzy rules. The number of fuzzy rules further increases when applied to complex systems involving more input and output parameters. Therefore, it is necessary to reduce the number of fuzzy rules to enhance system performance, especially in reducing computation time during system operation. Several advanced techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and others, are utilized to reduce the number of fuzzy rules. All these techniques are algorithms that require programming and cannot be used for manual design. Consequently, the Karnaugh Map (K-MAP) systematic approach technique is applied in this study to decrease the number of fuzzy rules for the fuzzy controller. The K-MAP is one of the methods used to simplify fuzzy logic rules, which can be applied or designed programmatically or manually. The K-MAP is much simpler and does not require knowledge of Boolean algebraic theorems. It involves a smaller number of steps compared to other solutions. This research involves two different case studies with varying numbers of input variables and one output variable. The performance of the selected case study is compared, and computation times have been improved.

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