Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Artificial neural networks for solving ordinary and partial differential equations
IEEE Transactions on Neural Networks
Neural-network methods for boundary value problems with irregular boundaries
IEEE Transactions on Neural Networks
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We present a technique for function approximation in a partitioned domain. In each of the partitions a form containing a Neural Network is utilized with parameterized boundary conditions. This parameterization renders feasible the parallelization of the computation. Conditions of continuity across the partitions are studied for the function itself and for a number of its derivatives. A comparison is made with traditional methods and the results are reported.