Two-stage iterative methods for the solution of linear systems are studied. Convergence of both stationary and nonstationary cases is analyzed when the coefficient matrix is Hermitian positive definite. Keywords—Linear systems, Hermitian matrices, Positive definite matrices, Iterative methods, Nonstationary methods, Two-stage methods.

We describe a method for generating sentences from “keywords” or “headwords”. This method consists of two main parts, candidate-text construction and evaluation. The construction part generates text sentences in the form of dependency trees by using complementary information to replace information that is missing because of a “knowledge gap” and other missing function words to generate natural ...

The Multiple Input Multiple output (MIMO) system are considered to be the strongest candidate for the maximum utilization of available bandwidth. In this paper, the MIMO system with the combination of Multi-Carrier Code Division Multiple Access (MC-CDMA) and Space Time Coding (STC) using the Alamouti’s scheme is considered. A Genetic Algorithm (GA) based receiver with an exceptional relationshi...

An approach for using voltage sources to produce a desired current pattern in an ACT-type EIT system is presented. An iterative adaptive algorithm generates the necessary voltage pattern that will result in the desired current pattern. The convergence of the algorithm is shown under the condition that the estimation error of the linear mapping from voltage to current is small. The simulation re...

Subdivision schemes are efficient tools for building curves and surfaces. For vector subdivision schemes, it is not so straightforward to prove more than the Hölder regularity of the limit function. On the other hand, Hermite subdivision schemes produce function vectors that consist of derivatives of a certain function, so that the notion of convergence automatically includes regularity of the ...

BP Neural Network has a longer training time and a slow convergence. To deal with the defects of BP Neural Network a modified BP algorithm is proposed in the paper. The algorithm is applied for the control of Inverted Pendulum, a highly non linear system inherently being open loop unstable. Levenberg-Marquardt algorithm is used for the training purpose. The training samples are being collected ...

In this paper a technique for increasing the convergence rate of fractionally spaced channel equalizer is proposed. Instead of symbol-spaced updating of the equalizer filter, a mechanism has been devised to update the filter at a higher rate. This ensures convergence of the equalizer filter at a higher rate and therefore less time-consuming. The proposed technique has been simulated and tested ...

The back propagation algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a training algorithm consisting of a learning rate and a momentum factor. The major drawbacks of above learning algorithm are the problems of local minima and slow convergence speeds. The addition of an extra term, called a proportional factor reduces the convergence of th...

The 1669-1670 Newton-Raphson’s method is still used to solve equations systems and unconstrained optimization problems. Since this method, some other algorithms inspired by Newton’s have been proposed: in 1839 Chebyshev developped a high order cubical convergence algorithm, and in 1967 Shamanskii proposed an acceleration of Newton’s method. By considering a Newton-type methods as displacement d...