Generally, the blind separation algorithms based on the subspace approach are very slow. In addition, they need a considerable computation e ort and time due to the estimation and the minimization of huge matrices. Previously, we proposed an adaptive subspace criterion to solve the blind separation problem [1]. The criterion has been minimized adaptively using a conjugate gradient algorithm [2]...

The real-time recurrent learning (RTRL) algorithm, which is originally proposed for training recurrent neural networks, requires a large number of iterations for convergence because a small learning rate should be used. While an obvious solution to this problem is to use a large learning rate, this could result in undesirable convergence characteristics. This paper attempts to improve the conve...

Abstract Machine learning algorithms can study existing data to perform specific tasks. One of the well-known machine is backpropagation algorithm, but this algorithm often provides poor convergence speed in training process and a long time. The purpose optimize standard using Beale-Powell conjugate gradient so that time needed achieve not too long, which later be used as reference information ...

In this paper we introduce a Riemannian algorithm for minimizing (or maximizing) a real-valued function J of complex-valued matrix argument W under the constraint that W is an n × n unitary matrix. This type of constrained optimization problem arises in many array and multi-channel signal processing applications. We propose a conjugate gradient (CG) algorithm on the Lie group of unitary matrice...