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Back-propagation neural networks (BPN) have been extensively used as global approximation tools in the context of approximate optimization.
Artificial neural networks (ANN) have been extensively used as global approximation tools in the context of approximate optimization.
We have carried out extensive computer simulations to assess the validity of the proposed approximation tools.
However, extensive numerical examples have shown that this type of algorithm can solve general MP problems of dimensions 10 15 (while the problems of such dimensions are already very hard to solve by the standard approximation tools) [5, 6, 35].
We present, by making use of multivariate approximation tools, a quantitative upper bound estimation on approximation accuracy of the simultaneous approximation of the FNNs in terms of the modulus of smoothness of the functions to be approximated.
Stochastic approximation tools can also be used to obtain a recursive estimate of the frame-by-frame throughput sample average as T ¯ m ( t + 1 ) = ( 1 - β t ) T ¯ m ( t ) + β t R m ( t ) N o T o T s. (15).
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The use of neural networks as global approximation tool in crashworthiness problems is here investigated.
It can also be utilized for cased and perforated wells as an approximation tool.
The use of neural networks as global approximation tool in accident reconstruction is here investigated.
An algebraic expression based on the mean value theorem is used as the vehicle for an approximation tool.
Since a meshless method is utilised as the approximation tool in the longitudinal direction of the strips, enforcing the essential boundary conditions requires special procedures.
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