ZNN models for computing matrix inverse based on hyperpower iterative methods
2017
Our goal is to investigate and exploit an analogy between the scaled
hyperpower family (SHPI family) of iterative methods for computing the
matrix inverse and the discretization of Zhang Neural Network (ZNN) models.
A class of ZNN models corresponding to the family of hyperpower iterative
methods for computing generalized inverses is defined on the basis of the
discovered analogy. The Simulink implementation in Matlab of the introduced
ZNN models is described in the case of scaled hyperpower methods of the
order 2 and 3. Convergence properties of the proposed ZNN models are
investigated as well as their numerical behavior.
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