Abstract
Fuzzy logic [26] is an alternative approach to control system design. Fuzzy logic works within the framework of set theory and is better at dealing with ambiguities. For example, three sets may be defined for a sensor: hard failure, soft failure, and no failure. The three sets may overlap and at any given time the sensor may have a degree of membership in each set. The degree of membership in each set can be used to determine what action to take. An algorithmic approach would have to assign a number to the state of the sensor. This could be problematic and not necessarily represent the actual state of the system. In effect, you would be applying a degree of fuzziness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ka Cheok et al. Fuzzy Logic-Based Smart Automatic Windshield Wiper. IEEE Control Systems, December 1996.
K Terano, Asai T., and M. Sugeno. Fuzzy Systems Theory and Its Applications. Academic Press, 1992.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Michael Paluszek and Stephanie Thomas
About this chapter
Cite this chapter
Paluszek, M., Thomas, S. (2019). Fuzzy Logic. In: MATLAB Machine Learning Recipes. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3916-2_6
Download citation
DOI: https://doi.org/10.1007/978-1-4842-3916-2_6
Published:
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-3915-5
Online ISBN: 978-1-4842-3916-2
eBook Packages: Professional and Applied ComputingApress Access BooksProfessional and Applied Computing (R0)