A Comparative Analysis of Fuzzy Inference Engines in Context of Profitability Control

Abstract

Fuzzy Inference engine is an important part of reasoning systems capable of extracting correct conclusions from approximate data. Among the many different types of inference engine that are commonly used are, product inference engine, Mamdani minimum inference engine, Lukasiewicz, Zadeh, Dienes-Rescher inference engines and root sum square inference engine. Fuzzy inference engine has found successful applications in a wide variety of fields, such as automatic control, data classification, decision analysis, expert engines, time series prediction, robotics, pattern  recognition, etc. This paper presents a comparative analysis of three fuzzy
inference engines, max-product, max-min and root sum in fuzzy  controllers using profitability control data. The presented results shows that RSS inference engine gives largest output membership function, while the product inference engine gives the smallest output membership function in this case; minimum inference engine is in between. This suggests that root sum square inference engine is one of the most promising strategies in profitability control.

Keywords: Max-Product Inference, Max-Min Inference, Root Sum Square Inference, Soft Computing, Membership Function, Profitability Control.