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An MS_CMAC neural network is a modular CMAC model designed to model smooth functional mapping.
The construction is also extended to tuples of more general operators allowing smooth functional calculii.
This method makes it possible to obtain model equations with smooth functional coefficients, describing the effect of the microstructure size.
A specific advantage of this method over any purely numerical method is that it offers a smooth, functional form of the solution over a time step.
This work introduces a revision, dubbed cubic Harris-Priester, which ensures continuous first derivatives, eliminates singularities, and adds a mechanism for introducing smooth functional dependencies on environmental conditions.
This method leads to model equations with smooth functional coefficients, which describe the effect of the microstructure size of these plates not only in dynamic but also in stationary problems.
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Consider now the set ({mathcal {S}}_{W}) of smooth functionals F=fbigl({W} varphi_{1}),{W}(varphi_{2}), ldots,{W} varphi_{n} bigr), (2.2) where the function f and all its derivatives are bounded (denoted by (fin C^{infty}_{b}({mathbb {R}}^{n}))) and (varphi_{i}in{mathcal {H}}).
The study bases itself on the most recent variational approaches to the smooth functionals which are defined on reflexive Banach spaces.
In [9], based on an abstract linking theorem for smooth functionals, they also established a multiplicity result on the existence of weak solutions for a nonlocal Neumann problem driven by a nonhomogeneous elliptic differential operator.
(2.2) Let us denote by (mathcal {S}) the set of smooth functionals of the form F=fbigl(B psi_{1}),B psi_{2}),ldots,B( psi_{n} bigr), where (fin C^{infty}_{b}({mathbb {R}}^{n})) and (psi_{i}in{mathcal {H}}).
A key component of this is the use of anisotropic polynomial order which admits evolving global bases for approximation in an efficient manner, leading to consistently stable approximation for a practical class of smooth functionals.
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