New zeroing neural dynamics models for diagonalization of symmetric matrix stream

Autor: Huanchang Huang, Yunong Zhang, Yihong Ling, Min Yang, Binbin Qiu, Jian Li
Rok vydání: 2019
Předmět:
Zdroj: Numerical Algorithms. 85:849-866
ISSN: 1572-9265
1017-1398
DOI: 10.1007/s11075-019-00840-5
Popis: In this paper, the problem of diagonalizing a symmetric matrix stream (or say, time-varying matrix) is investigated. To fulfill our goal of diagonalization, two error functions are constructed. By making the error functions converge to zero with zeroing neural dynamics (ZND) design formulas, a continuous ZND model is established and its effectiveness is then substantiated by simulative results. Furthermore, a Zhang et al. discretization (ZeaD) formula with high precision is developed to discretize the continuous ZND model. Thus, a new 5-point discrete ZND (DZND) model is further proposed for diagonalization of matrix stream. Theoretical analyses prove the stability and convergence of the 5-point DZND model. In addition, simulative experiments are carried out, of which the results substantiate not only the efficacy of the proposed 5-point DZND model but also its higher computational precision as compared with the conventional Euler-type and 4-point DZND models for diagonalization of symmetric matrix stream.
Databáze: OpenAIRE
Externí odkaz: