Construction of an analytical method for limiting the complexity of neural-fuzzy models with guaranteed accuracy

Construction of an analytical method for limiting the complexity of neural-fuzzy models with guaranteed accuracy

We have proposed an analytical method for limiting the complexity of neural-fuzzy models that provide for the guaranteed accuracy of their implementation when approximating functions with two or more derivatives. The method makes it possible to determine the required minimal number of parameters for...

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Bibliographic Details
Main Authors: Borys Sytnik, Volodymyr Bryksin, Sergiy Yatsko, Yaroslav Vashchenko
Format: Article
Language:English
Published: PC Technology Center 2019-04-01
Series:Eastern-European Journal of Enterprise Technologies
Subjects:
approximation
guaranteed accuracy
fuzzy logic
neural networks
imitation simulation
Online Access:http://journals.uran.ua/eejet/article/view/160719

Internet

http://journals.uran.ua/eejet/article/view/160719

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