Train RBF networks with a hybrid genetic algorithm-Reference-Cited by-同舟云学术

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Train RBF networks with a hybrid genetic algorithm

Published:2021-08-13 Issue: Volume: Page:
ISSN:1864-5909
Container-title:Evolutionary Intelligence
language:en
Short-container-title:Evol. Intel.

Author:

Tsoulos Ioannis G.,Anastasopoulos Nikolaos,Ntritsos Georgios,Tzallas Alexandros

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s12065-021-00654-2.pdf

Reference50 articles.

1. Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

2. Rokach L, Maimon O (2005) Decision trees. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, Boston, MA

3. Huang XL, Ma X, Hu F (2018) Editorial: machine learning and intelligent communications. Mobile Netw Appl 23:68–70

4. Chen ZY, Kuo RJ (2019) Combining SOM and evolutionary computation algorithms for RBF neural network training. J Intell Manuf 30:1137–1154

5. Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3:246–257

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