Approximation capabilities of multilayer feedforward networks
Neural Networks
Approximation and radial-basis-function networks
Neural Computation
Can fuzzy neural nets approximate continuous fuzzy functions?
Fuzzy Sets and Systems
Analyses of regular fuzzy neural networks for approximation capabilities
Fuzzy Sets and Systems
Letters: Convex incremental extreme learning machine
Neurocomputing
Finding the differential characteristics of block ciphers with neural networks
Information Sciences: an International Journal
Information Sciences: an International Journal
Improving artificial neural networks' performance in seasonal time series forecasting
Information Sciences: an International Journal
Approximation accuracy analysis of fuzzy systems as function approximators
IEEE Transactions on Fuzzy Systems
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Approximation capability in C(R¯n) by multilayer feedforward networks and related problems
IEEE Transactions on Neural Networks
Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization
Information Sciences: an International Journal
The forecasting model based on fuzzy novel ν-support vector machine
Expert Systems with Applications: An International Journal
Approximation of fuzzy functions by regular fuzzy neural networks
Fuzzy Sets and Systems
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This paper first introduces a piecewise linear interpolation method for fuzzy-valued functions. Based on this, we present a concrete approximation procedure to show the capability of four-layer regular fuzzy neural networks to perform approximation on the set of all d"p continuous fuzzy-valued functions. This approach can also be used to approximate d"~ continuous fuzzy-valued functions. An example is given to illustrate the approximation procedure.