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The paper deals with investigating approximation abilities of a special class of discrete-time dynamic neural networks.
Rule based systems are a powerful and well-studied form of knowledge representation, due to their approximation abilities and interpretability.
We selected the ANN architectures that demonstrated the most promising global approximation abilities during the training stage (the M4 model series and M5n) to apply for an independent dataset for the year 2006.
We may recall that polynomial neural networks (including QNU) are attractive due to the reliable theoretical results for their universal approximation abilities according to the Weierstrass theorem and for their generalization power measured by the Vapnik-Chervonenkis (VC) dimension [ 27].
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BPNN is a widely used machine learning technique for classification due to its remarkable function approximation ability.
This paper proposes a CMAC-based fuzzy logic controller (FLC) with a fast learning capability and an accurate approximation ability.
The improvement was mainly due to the RBFN's strong approximation ability and its superior tolerance to experimental noises/uncertainties.
Relying on the function approximation ability and fast convergence of RBFNN, the transmission energy efficiency can be maximized given arbitrary external interferences in working band.
By combining adaptive backstepping technique with neural networks approximation ability, an adaptive neural control algorithm is presented for the systems under consideration.
In order to guarantee the approximation ability for those main uncertainties, the input space and parallel structure are carefully designed for AFCMAC compensator.
The latter model showed better performance in simulations despite worse approximation ability, which can be attributed to its micromechanically-motivated formulation.
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