Abstract
In general, this work leads to the conclusion that the use of generalized type-2 fuzzy systems can be a good choice when there is a high level of uncertainty in the problem. In other words, generalized type-2 fuzzy logic allows for better modeling of uncertainty, because it gives more degrees of freedom in comparison to interval type-2 and type-1 fuzzy logic. The complex nature of the uncertainty encountered in the real world indicates that generalized type-2 is needed in real-world devices and applications, in particular in the image processing area that is the case study in this book, because the devices that capture digital images are always exposed to external interference adding high noise levels or uncertainty to the images.
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 The Author(s)
About this chapter
Cite this chapter
Gonzalez, C.I., Melin, P., Castro, J.R., Castillo, O. (2017). Conclusions. In: Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-53994-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-53994-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53993-5
Online ISBN: 978-3-319-53994-2
eBook Packages: EngineeringEngineering (R0)