Finding the ordered roots of arbitrary polynomials using constrained partitioning neural networks

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.