TL;DR
This paper introduces a novel stochastic fuzzy controller approach that utilizes probability density functions to represent membership functions, differing from traditional methods based on binary random signals.
Contribution
The paper proposes a new method for fuzzy control design using probability density functions, expanding the theoretical framework of stochastic fuzzy controllers.
Findings
Demonstrates the feasibility of using probability density functions for membership representation.
Provides a comparative analysis with traditional binary stochastic methods.
Lays groundwork for more flexible fuzzy control systems.
Abstract
A standard approach to building a fuzzy controller based on stochastic logic uses binary random signals with an average (expected value of a random variable) in the range [0, 1]. A different approach is presented, founded on a representation of the membership functions with the probability density functions.
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Taxonomy
TopicsFuzzy Logic and Control Systems · Neural Networks and Applications · Fuzzy Systems and Optimization