Finding the maximum modulus roots of polynomials based on constrained neural networks

This paper focuses on how to find the maximum modulus root (MMR) (real or complex) of an arbitrary polynomial. Efficient solution to this problem is important for many fields including neural computation and digital signal processing etc. We present neural networks technique for solving this problem. Our neural root finder (NRF) is designed based on partitioning feedforward neural networks (FNN) trained with a constrained learning algorithm (CLA) by imposing the a priori information about the root moment from polynomial into the error cost function. Experimental results show that this neural root-finding method is able to find the maximum modulus roots of polynomials rapidly and efficiently.