Radial Basis Function Networks (RBFNs)

Atefeh Hemmati, Amir Masoud Rahmani, Fatemeh Bazikar, Hossein Moosaei and Panos M. Pardalos
Additional contact information
Atefeh Hemmati: Islamic Azad University, Department of Computer Engineering, SR.C
Amir Masoud Rahmani: National Yunlin University of Science and Technology, Future Technology Research Center
Fatemeh Bazikar: Alzahra University, Department of Computer Science, Faculty of Mathematical Sciences
Hossein Moosaei: Jan Evangelista Purkyně University, Department of Informatics, Faculty of Science
Panos M. Pardalos: University of Florida, Department of Industrial & Systems Engineering

Chapter Chapter 7 in Optimization Techniques for Deep Learning, 2026, pp 99-108 from Springer

Abstract: Abstract Saroj Kaushik’s definition is deceptively simple, and that is the beauty of RBFs. Distance becomes meaning. A single idea, “How far am I from what I know?” turns into a powerful, interpretable model for approximation, control, and classification. While deep networks dominate headlines, RBFNs thrive in silence:

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-032-20703-6_7

Ordering information: This item can be ordered from
http://www.springer.com/9783032207036

DOI: 10.1007/978-3-032-20703-6_7

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().