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Title: Finding the ordered roots of arbitrary polynomials using constrained partitioning neural networks
Authors: Huang, DS
Law, KCK
Wong, HS
Keywords: Constraint theory
Filtering theory
Neural nets
Polynomial approximation
Signal processing
Issue Date: 2003
Source: Proceedings of the International Joint Conference on Neural Networks (IJCNN'2003), Portland, Oregon, 20-24 July 2003, p. 1098-1103 How to cite?
Abstract: This paper proposed a partitioning neural root finder (PNRF) to find the minimum modulus (real or complex) roots of an arbitrary polynomial by imposing a minimum m order root moment (RM) into the constrained learning algorithm (CLA), where the constraint "the minimum m order RM" will ensure the minimum modulus root to be obtained. If the PNRF is recursively updated, the ordered roots from minimum modulus to maximum one can be achieved. Simulations show that this partitioning neural root-finding method is indeed able to find the minimum modulus root and the ordered roots of arbitrary polynomials readily and efficiently.
ISBN: 0-7803-7898-9
DOI: 10.1109/IJCNN.2003.1223844
Appears in Collections:Conference Paper

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