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...

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Published in2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03) Vol. 2; pp. II - 797
Main Authors De-Shuang Huang, Ip, H.H.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Algorithm design and analysis
Computer science
Cost function
Digital signal processing
Feedforward neural networks
Intelligent networks
Machine intelligence
Neural networks
Polynomials
Signal processing
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Summary: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.
ISBN:9780780376632
0780376633
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2003.1202487