Modified Oja's algorithms for principal subspace and minor subspace extraction
Neural Processing Letters
Complex recurrent neural network for computing the inverse and pseudo-inverse of the complex matrix
Applied Mathematics and Computation
Journal of Computational and Applied Mathematics
A PCA approach for fast retrieval of structural patterns inattributed graphs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Computers & Mathematics with Applications
Low-rank matrix decomposition in L1-norm by dynamic systems
Image and Vision Computing
A novel neural network approach for computing eigen-pairs of real antisymmetric matrices
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Hi-index | 5.23 |
Quick extraction of the largest modulus eigenvalues of a real antisymmetric matrix is important for some engineering applications. As neural network runs in concurrent and asynchronous manner in essence, using it to complete this calculation can achieve high speed. This paper introduces a concise functional neural network (FNN), which can be equivalently transformed into a complex differential equation, to do this work. After obtaining the analytic solution of the equation, the convergence behaviors of this FNN are discussed. Simulation result indicates that with general initial complex values, the network will converge to the complex eigenvector which corresponds to the eigenvalue whose imaginary part is positive, and modulus is the largest of all eigenvalues. Comparing with other neural networks designed for the like aim, this network is applicable to real skew matrices.