Adaptive Generalized Eigenvector Estimating Algorithm for Hermitian Matrix Pencil

Generalized eigenvector plays an essential role in the signal processing field.In this paper,we present a novel neural network learning algorithm for estimating the generalized eigenvector of a Hermitian matrix pencil.Differently from some traditional algorithms,which need to select the proper values of learning rates before using,the proposed algorithm does not need a learning rate and is very suitable for real applications.Through analyzing all of the equilibrium points,it is proven that if and only if the weight vector of the neural network is equal to the generalized eigenvector corresponding to the largest generalized eigenvalue of a Hermitian matrix pencil,the proposed algorithm reaches to convergence status.By using the deterministic discretetime(DDT)method,some convergence conditions,which can be satisfied with probability 1,are also obtained to guarantee its convergence.Simulation results show that the proposed algorithm has a fast convergence speed and good numerical stability.The real application demonstrates its effectiveness in tracking the optimal vector of beamforming.

Efficient Anomalous Forcings for Linear Problems

For linear forcing problems, a method is developed to provide a set of forcing modes which form a complete orthonormal basis for the finite-time response to steady forcing in the energy inner product space. The forcing modes are found by calculating eigenvectors of a positive definite and symmetric matrix determined from given equations of motion. The amplitude of responses to forcing modes is given in terms of the associated eigenvalues. This method is used in a nondivergent barotropic model linearized about the 300 hPa zonally-varying climatological flow both for northern summertime and wintertime. The results show that the amplitude of response varies considerably with different forcing modes. Only a few of forcing modes associated with the leading eigenvalues, called efficient forcing mode, can excite significant response. The efficient forcing modes possess highly localized spatial structure with wavetrain appearance. Most of the efficient forcings are located to the south of regions of the jet cores. The forcings located over polar regions are also efficient. In addition, the response is larger in wintertime than in summertime for a given forcing.

Specification of Initial Shapes for Dynamic Implicit Curve/Surface Reconstruction

The dynamic implicit curve/surface reconstruction demands no special requirement on the initial shapes in general. In order to speed up the iteration in the reconstruction, we discuss how to specify the initial shapes so as to reflect the geometric information and the topology structure of the given data. The basic idea is based on the combination of the distance function and the generalized eigenvector fitting model. Keywords Sampson distance, generalized eigenvector fitting, dynamic implicit surface reconstruction