A novel complex neural network model for computing the largest real part of eigenvalues and the corresponding eigenvector of a real matrix
Rong Ye, Hang Tan, Xuesong Liang, Ping-Li Wan · 2017
A novel complex neural network modelwasproposed, which can be used to compute the largestreal part of eigenvalues and the corresponding eigenvector of a general real matrixin this work.Because of the smart regulatory factorof the model, the largest real part also can be extracted in the case of all the real parts of eigenvalues less than 0. Meanwhile, the presented paper provides a rigorous mathematical proof for its convergence for a more clear understanding of network dynamic behaviors relating to the computation of the eigenvector and the eigenvalue.Numerical example showsthat the proposed model has good performance for a general real matrix.