Approximation of fuzzy-valued functions by regular fuzzy neural networks and the accuracy analysis
From MaRDI portal
Publication:894316
DOI10.1007/s00500-014-1232-xzbMath1371.92016OpenAlexW2072467248MaRDI QIDQ894316
Publication date: 30 November 2015
Published in: Soft Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00500-014-1232-x
Fuzzy control/observation systems (93C42) Neural networks for/in biological studies, artificial life and related topics (92B20)
Related Items (3)
Characterizations of compact sets in fuzzy set spaces with \(L_p\) metric ⋮ Approximation of fuzzy numbers using the convolution method ⋮ Characterizations of endograph metric and \(\Gamma\)-convergence on fuzzy sets
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Approximation of fuzzy functions by regular fuzzy neural networks
- Observer-based adaptive fuzzy tracking control for a class of uncertain nonlinear MIMO systems
- Constructive approximate interpolation by neural networks in the metric space
- Solving fuzzy equations using evolutionary algorithms and neural nets
- Finding the differential characteristics of block ciphers with neural networks
- Some notes on Zadeh's extensions
- Analyses of regular fuzzy neural networks for approximation capabilities
- Approximation analysis of feedforward regular fuzzy neural network with two hidden layers
- Can fuzzy neural nets approximate continuous fuzzy functions?
- Generalized extreme learning machine acting on a metric space
- Approximation of level continuous fuzzy-valued functions by multilayer regular fuzzy neural networks
- Approximation capabilities of multilayer fuzzy neural networks on the set of fuzzy-valued functions
- Universal approximation bounds for superpositions of a sigmoidal function
- Learning representations by back-propagating errors
- Universal approximations of continuous fuzzy-valued functions by multi-layer regular fuzzy neural networks
This page was built for publication: Approximation of fuzzy-valued functions by regular fuzzy neural networks and the accuracy analysis