Toufik Bouden

Fuzzy adaptive synchronization of uncertain fractional-order chaotic systems

This paper deals with the issue of projective synchronization of two distinct fractional-order chaotic systems with the presence of both uncertain dynamics and external disturbances. More precisely, this study is an attempt to investigate a novel fuzzy adaptive controller for achieving an appropriate projective synchronization of uncertain fractional-order chaotic systems. The adaptive fuzzy systems are utilized to online estimate unknown system nonlinearities. The proposed controller, which is derived based on a Lyapunov approach, is continuous and ensures the stability of the closed-loop system and the exponential convergence of the underlying synchronization errors to a small residual set. Finally, three simulation examples are provided to verify the effectiveness of the proposed synchronization method.

Fuzzy generalized projective synchronization of incommensurate fractional-order chaotic systems

This paper proposes a novel fuzzy adaptive controller for achieving an appropriate generalized projective synchronization (GPS) of two incommensurate fractional-order chaotic systems. The master system and the slave system, considered here, are assumed to be with non-identical structure, external dynamical disturbances, uncertain models and distinct fractional-orders. The adaptive fuzzy systems are used for estimating some unknown nonlinear functions. A Lyapunov approach is adopted for deriving the parameter adaptation laws and proving the stability of the closed-loop system. Under some mild assumptions, the proposed controller can guarantee all the signals in the closed-loop system remain bounded and the underlying synchronization errors asymptotically converge towards a small of neighborhood of the origin. Finally, some numerical experiment results are presented to illustrate the effectiveness of the proposed synchronization scheme.

Adaptive fuzzy control-based projective synchronization of uncertain nonaffine chaotic systems

This article aims to introduce a projective synchronization approach based on adaptive fuzzy control for a class of perturbed uncertain multivariable nonaffine chaotic systems. The fuzzy-logic systems are employed to approximate online the uncertain functions. A Lyapunov approach is used to design the parameter adaptation laws and to demonstrate the boundedness of all signals of the closed-loop system as well as the convergence of the synchronization errors to bounded residual sets. Finally, numerical simulation results are presented to verify the feasibility and effectiveness of the proposed synchronization system based on fuzzy adaptive controller.

Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

In this paper, the projective synchronization problem of two fractional-order different chaotic (or hyperchaotic) systems with both uncertain dynamics and external disturbances is considered. More particularly, a fuzzy adaptive control system is investigated for achieving an appropriate projective synchronization of unknown fractional-order chaotic systems. The adaptive fuzzy logic systems are used to approximate some uncertain nonlinear functions appearing in the system model. These latter are augmented by a robust control term to compensate for the unavoidable fuzzy approximation errors and external disturbances as well as residual error due to the use of the so-called e-modification in the adaptive laws. A Lyapunov approach is adopted for the design of the parameter adaptation laws and the proof of the corresponding stability as well as the asymptotic convergence of the underlying synchronization errors towards zero. The effectiveness of the proposed synchronization system is illustrated through numerical experiment results.

Fuzzy adaptive synchronization of a class of fractional-order chaotic systems

This paper deals with the issue of projective synchronization of two distinct fractional-order chaotic systems with the presence of both uncertain dynamics and external disturbances. More precisely, this study is an attempt to investigate a novel fuzzy adaptive controller for achieving an appropriate projective synchronization of uncertain fractional-order chaotic systems. The adaptive fuzzy systems are utilized to online estimate unknown system nonlinearities. The proposed controller, which is derived based on a Lyapunov approach, is continuous and ensures the stability of the closed-loop system and the exponential convergence of the underlying synchronization errors to a small residual set. Finally, a simulation example is provided to verify the effectiveness of the proposed synchronization method.

Fuzzy Adaptive Synchronization of Incommensurate Fractional-Order Chaotic Systems

This chapter presents a fuzzy adaptive control scheme for achieving a generalized projective synchronization (GPS) of two incommensurate fractional-order chaotic systems. The master system and the slave system are assumed to be with non-identical structure, external dynamical disturbances, uncertain models and distinct fractional-orders. The adaptive fuzzy systems are employed for approximating some unknown nonlinear functions. Lyapunov method is adopted for deriving the adaptation laws and proving the stability of the closed-loop system. Under mild assumptions, the proposed control scheme can guarantee all the signals in the closed-loop system remain bounded and the synchronization errors converge asymptotically towards a small of neighbourhood of the origin. Finally, numerical experiment results are presented to show the effectiveness of the proposed synchronization scheme.

Change Detection in Urban Land Cover Using Landsat Images Satellites, A Case Study in Algiers Town

In this paper three images of Land sat TM data of 1987, 2001 and 2010 being used to analyze the land cover and land use LC/LU changes in Algiers areas. The paper followed the expansion of urban tissue and its effect of decreasing agricultural and bare soil lands using difference soil adjusted vegetation index DSAVI, difference Greenness Tasseled Cap transformation DGTCT and post classification of multi-spectral and multi-temporal L5 and L7 Land sat satellite. The TM reflectances images have been transformed to ETM+ reflectances images using a regression method. The Maximum Likelihood algorithm is used to classify the reflectance images into the thematic urban, vegetation and bare soil map. For evaluating the classification and its assessment accuracy the neural net classification is carried out. The classified maps were then used as inputs to perform the post classification change detection. The Threshold changes are calculated for both DSAVI and DGTCT based on the means and standards deviation images. After that, the unchanged area, changes spaces are quantified and mapped. The proposed method is based on the study which mentioned that the urban tissue was faster growing with an annually growth of 0.5% and both DSAVI, DGTCT, post classification are useful for LC/LU change detection. Our proposed method is applied to Algiers town in North Africa.

Face-iris multi-modal biometric system using multi-resolution Log-Gabor filter with spectral regression kernel discriminant analysis.

A multi-modal biometric system is used to verify or identify a person by exploiting information of more than one biometric modality. It combines the strengths of the unimodal biometric system to solve their limitations. This study proposes schemes of multi-modal biometric system based on texture information extracted from face and two iris (left and right) using hybrid level of fusion. Feature extraction is the key step to get a robust recognition system. Multi-resolution two-dimensional Log-Gabor filter combined with spectral regression kernel discriminant analysis is exploited to extract features from both face and iris modalities. These features are used in the fusion and the classification process. The proposed schemes were tested using CASIA Iris Distance database in the verification mode. The experimental results show that the proposed multi-modal biometric system yields attractive performances of up to 0.24% in terms of equal error rate and outperforms the recent similar existing state-of-the-art methods.

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Abstract Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as built-up land features extraction index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the spectral discrimination index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu’s thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%.

A deconvolution scheme for the stochastic metabolic/hemodynamic model (sMHM) based on the square root cubature Kalman filter and maximum likelihood estimation

Abstract Based on clinical data collected using different brain imaging and recording techniques, brain researchers built mathematical models of the activity in the human brain. To test these models they simulate them by performing on those models a virtual brain experiment, and compare the outputs from those with the real brain activity recordings. The models can be a basis for understanding what goes wrong in brain diseases and brain disorders and potentially help to create new drugs for these conditions. Metabolic Hemodynamic Model (MHM) is one of these models that describes the changes in metabolic and hemodynamic responses during functional brain activity, formulated in a continuous-discrete state space form. MHM calibration is a decisive step for successfully capture the changes in the latent variables that can not be directly observed and predicting the brain activity related to these changes, this requires having suitable techniques that permit us to estimate both the hidden states and parameters of the MHM. The method proposed in this paper is a combination of the Square Root Cubature Kalman Filter (SCKF) and Maximum Likelihood Estimation (MLE), it uses gradient-based optimization algorithms for optimizing the objective function. Numerical results obtained with simulated data are presented to illustrate the effectiveness of the proposed method to estimate the states, parameters and regenerating the BOLD signal even when the data are contaminated with high noise level. In the proposed method, it will be explained how the gradient can be calculated with a new developed SCKF-like recursion and the result, whenever there is a vast amount of data, so much less time can be spent analyzing it compared to the time spent when the data is analyzed using finite differences. The goal of these attempts is to construct a formal system that will produce theoretical results that are corresponding to what is found in reality.