This paper discusses the robust filtering problems for linear discrete-time systems with polytopic parameter uncertainty under the H2 and H , performance. We aim to derive a less conservative design than existing sufficient Iinear matrix inequality (LMI) based conditions. It is shown that a more efficient evaluation of robust HZ or H , performance can be obtained by a matrix inequality conditio...

Computer Applications in Power System is place an important role to verify the system performance and real time monitoring systems. Most of the Electrical system uses LABVIEW and other software to monitor real time electrical transition. In this paper we are focusing on Power Electronic Conversion system. Power Electronic System is easily simulated using MATLAB Software. Here we are giving easy...

In this paper, we provide an overview of some recently introduced principles and ideas for speech enhancement with linear filtering and explore how these are related and how they can be used in various applications. This is done in a general framework where the speech enhancement problem is stated as a signal vector estimation problem, i.e., with a filter matrix, where the estimate is obtained ...

This paper is concerned with the H2 robust filtering problem for discrete-time Markov jump linear systems (MJLS) with transition probability matrix affected by uncertainties. Differently from previous approaches in the literature, the proposed strategy presents a systematic way to handle, simultaneously, different types of uncertainties commonly appearing in the transition probability matrix of...

In real world implementations of blind source separation and deconvolution, the mixing takes place in continuous time. In the models normally considered, discrete time sampling is implicitly assumed to provide a mixing filter matrix from a suitable demixing filter matrix which can be learned given an appropriate algorithm. In this paper, we consider the implications of trying to separate and de...

This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The scheme provides an efficient way to deal with filters with large number of elements. It consists of a bank of full-rank adaptive filters that forms a transformation matrix and an adaptive reduced-rank filter that operates at the output of the bank of fil...

In this paper we present a neural network extended Kalman filter for modeling noisy financial time series. The neural network is employed to estimate the nonlinear dynamics of the extended Kalman filter. Conditions for the neural network weight matrix are provided to guarantee the stability of the filter. The extended Kalman filter presented is designed to filter three types of noise commonly o...

A density matrix based time-dependent density functional theory is extended in the present work. Chebyshev expansion is introduced to propagate the linear response of the reduced single-electron density matrix upon the application of a time-domain delta-type external potential. The Chebyshev expansion method is more efficient and accurate than the previous fourth-order Runge-Kutta method and re...

This paper discusses the fixed-order robust filtering problem for a class of Markovian jump linear systems with uncertain switching probabilities. The uncertainties under consideration are assumed to be norm-bounded in the system matrices and to be elementwise bounded in the mode transition rate matrix, respectively. First, a criterion based on linear matrix inequalities is provided for testing...