Neural networks with problem decomposition for finding real roots of polynomials
De-Shuang Huang, Zheru Chi · 2005
This paper proposes applying feedforward neural networks (FNN) with problem decomposition and constrained learning to finding the real roots of polynomials. In order to alleviate ihe load of the computational complexity for high order polynomials, this network model is extended to one which works recursively with a small number of the real roots of a polynomial (less than the total number of roots to be found) obtained at a time. The recursive formulae for finding i real roots at a time are presented Finallx some computer simulaiion results are reported.