Piecewise Neural Networks for Function Approximation, Cast in a Form Suitable for Parallel Computation | Zendy

Ioannis G. Tsoulos | Zendy; I.E. Lagaris | Zendy; Aristidis Likas | Zendy

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Piecewise Neural Networks for Function Approximation, Cast in a Form Suitable for Parallel Computation

Author(s) -

Ioannis G. Tsoulos,

I.E. Lagaris,

Aristidis Likas

Publication year - 2002

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-43472-0

DOI - 10.1007/3-540-46014-4_28

Subject(s) - parameterized complexity , computation , computer science , piecewise , artificial neural network , function (biology) , domain (mathematical analysis) , boundary (topology) , function approximation , parallel computing , activation function , algorithm , mathematics , mathematical optimization , artificial intelligence , mathematical analysis , evolutionary biology , biology

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.

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