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Fuzzy logic is used as a language in which to express an uncertain model, instead of as an universal approximator.
The heat released by the reaction is estimated via an universal approximator, based on radial basis functions, whose weights are adjusted on-line.
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Then Peng and Chen [16] proved that an uncertain system is a universal approximator.
A fuzzy logic system or fuzzy system is a universal approximator which provides a rule-based mapping between the input and the output space, while classical approaches make use of analytic harmonic functions to solve the navigation problem.
The standard additive model (SAM), a common formulation of a fuzzy logic system, is a universal approximator.
With randomly generated weights between input and hidden layers, a random vector functional link network is a universal approximator for continuous functions on compact sets with fast learning property.
In [27], authors consider a multi-layered feed-forward neural network as a "universal approximator".
In this article, the reason of adopting the Gaussian membership function is that it can be a universal approximator of any nonlinear functions on a compact set [23].
An ANN consisting of an input layer, an output layer and one or more hidden layer could be adequate as a universal approximator of any nonlinear function [ 11, 13].
RVFL network is a universal approximator with good efficiency because of the randomly generated weights between input and hidden layers and the close form solution for parameter computation.
The Radial Basis Function Networks (RBFN) model has been successfully applied to different application scenarios as a universal approximator because of its simple architecture and online training capability.
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