Entity

Time filter

Source Type

M’Sila, Algeria

A direct adaptive control algorithm, based on neural networks (NN) is presented for a class of single input single output (SISO) nonlinear systems. The proposed controller is implemented without a priori knowledge of the nonlinear systems; and only the output of the system is considered available for measurement. Contrary to the approaches available in the literature, in the proposed controller, the updating signal used in the adaptive laws is an estimate of the control error, which is directly related to the NN weights instead of the tracking error. A fuzzy inference system (FIS) is introduced to get an estimate of the control error. Without any additional control term to the NN adaptive controller, all the signals involved in the closed loop are proven to be exponentially bounded and hence the stability of the system. Simulation results demonstrate the effectiveness of the proposed approach. © 2012 Elsevier Ltd. Source


Si Abdallah M.,University of ila | Zeghmati B.,University of Perpignan
Fluid Dynamics and Materials Processing | Year: 2014

In the present work, a numerical analysis is performed of the combined effects of (opposing) thermal and solutal buoyancy in the presence of a wavy (vertical) surface. The boundary layer equations and related boundary conditions are discretized using a finite volume scheme and solved numerically using a Gauss-Seidel algorithm. The influence of the wavy geometry (in terms of related wavelength L and amplitude a) and the buoyancy ratio N on the local Nusselt and Sherwood numbers and on the skin-friction coefficient are studied in detail. Results show that when PrSc, the flow is completely perturbed; the thickness of the mass boundary layer is larger than that of the thermal boundary layer. © 2014 Tech Science Press. Source


Barra S.,University of Batna | Dendouga A.,Center for Development of Advanced Technologies | Kouda S.,University of ila | Bouguechal N.-E.,University of Batna
Journal of Circuits, Systems and Computers | Year: 2013

The present work analyses the non-ideal effects of pipelined analog-to-digital converters (ADCs), also sometimes referred to as pipeline ADCs, including the non-ideal effects in operational amplifiers (op-amps or OAs), switches and sampling circuits. We study these nonlinear effects in pipelined ADCs built using CMOS technology and switched-capacitor (SC) techniques. The proposed improved model of a pipelined ADC includes most of the non-idealities which affect its performance. This model, simulated using MATLAB, can determine the basic blocks specifications that allow the designer to meet given data converter requirements. © 2013 World Scientific Publishing Company. Source


Benmehdi S.,University of Bourdj Bouarreridj | Makarava N.,University of Potsdam | Benhamidouche N.,University of ila | Holschneider M.,University of Potsdam
Nonlinear Processes in Geophysics | Year: 2011

The aim of this paper is to estimate the Hurst parameter of Fractional Gaussian Noise (FGN) using Bayesian inference. We propose an estimation technique that takes into account the full correlation structure of this process. Instead of using the integrated time series and then applying an estimator for its Hurst exponent, we propose to use the noise signal directly. As an application we analyze the time series of the Nile River, where we find a posterior distribution which is compatible with previous findings. In addition, our technique provides natural error bars for the Hurst exponent. © 2011 Author(s). Source


Chemachema M.,University of ila | Belarbi K.,University of Mentouri Constantine
International Journal of Systems Science | Year: 2011

A new approach of direct adaptive control of single input single output nonlinear systems in affine form using single-hidden layer neural network (NN) is introduced. In contrast to the algorithms in the literature, the weights adaptation laws are based on the control error and not on the tracking error or its filtered version. Since the control error is being expressed in terms of the NN controller, hence its weights updating laws are obtained via back-propagation concept. A fuzzy inference system (FIS) with heuristically defined rules is introduced to provide an estimate of this error based on the past history of the system behaviour. The stability of the closed loop is studied using Lyapunov theory. A fixed structure is then proposed for the FIS and the design parameters reduce to the parameters of the NN. The method is reproducible and does not require any pre-training of the network weights. © 2011 Taylor & Francis. Source

Discover hidden collaborations