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This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method and some existing techniques are given. They clearly show that the RCGA identification method performs better in terms of precision, time of convergence and simplicity of programming.

In this paper, we present a method for edge segmentation of satellite images based on 2-D Phase Congruency (PC) model. The proposed approach is composed by two steps: The contextual non linear smoothing algorithm (CNLS) is used to smooth the input images. Then, the 2D stretched Gabor filter (S-G filter) based on proposed angular variation is developed in order to avoid the multiple responses in the previous work. An assessment of our proposed method performance is provided in terms of accuracy of satellite image edge segmentation. The proposed method is compared with others known approaches. Keywords--Edge segmentation, Phase congruency model, Satellite images, Stretched Gabor filter, I. INTRODUCTION IN computer vision object detection have a significant role in image analysis. Several gradient-based edge detector methods have been developed. These methods are sensitive to edge magnitude, smoothness [...]

This paper deals with three control techniques for a three-phase three-level neutral-point-clamped (NPC) boost rectifier to study their relative performance. Linear, nonlinear, and nonlinear model reference adaptive control (MRAC) methods are developed to control power factor (PF) and regulate output and neutral point voltages. These controllers are designed in Simulink and implemented in real time using the DS1104 DSP of dSPACE for validation on a 1.2-kW prototype of an NPC boost rectifier operating at 1.92 kHz. The performance of boost converter with three control methods has been investigated respectively in steady state in terms of line-current harmonic distortion, efficiency, and PF and during transients such as load steps, utility disturbances, reactive power control, and dc-bus voltage tracking behavior. The linear PI controllers are characterized by reduced complexity but poor performance, whereas the nonlinear control technique has improved the converter performance significantly, while nonlinear MRAC exhibits much better performance in a wide operating range. Index Terms--Converter, digital implementation, linear control, nonlinear control, parametric variations, rectifier.

In this paper, the design and the implementation of a model reference adaptive control (MRAC) applied to a three-phase three-level neutral-point-clamped (NPC) boost rectifier are presented. This control strategy is developed with a view to regulate dc output and neutral point voltages and to reduce the influence of parameter variations while maintaining unity power factor. A non-linear multiple-input multiple-output (MIMO) state space model of the rectifier is then developed in dq0 reference frame. The proposed controller is based on the use of a feedback linearization technique followed by a robust MRAC scheme allowing the design of a suitable controller applied to the plant. The control law is designed in Simulink/Matlab and applied to the converter via a 1920-Hz pulse width modulator both executed in real time using the DS1104 DSP of dSPACE. A 1.25 kW laboratory prototype is developed for validation. The experimental results are given for different operating conditions: nominal power operation, balanced and unbalanced dc load steps, boost inductor variation, and reactive power control. The proposed control law performs perfectly in a wide operation range giving low output voltage ripple, low line-current THD, a small overshoot and a fast settling time under system parameters variation. Index Terms--Model reference adaptive control (MRAC), multiple-input multiple-output (MIMO), neutral-point-clamped (NPC).

This paper presents the design and implementation of a nonlinear control strategy for a three-phase three-level neutral-point-clamped boost rectifier. The adopted control consists of nonlinear feedback linearization technique. The nonlinear state-space model of the rectifier was obtained in the dqO reference frame. The input/output feedback linearization is then applied and the linearizing control law is derived. Therefore, the resulting model is linearized and decoupled in three independent subsystems. Afterwards, the stabilizing controllers are designed based on linear techniques to control line currents, output, and neutral point voltages. The control law is designed using Simulink/Matlab and applied to the converter via a 1.8-kHz pulsewidth modulator (PWM). Both control law and PWM signals are executed in real time using the DS1104 DSP of dSPACE. A 1.2 kW laboratory prototype is built for validation purposes. The proposed control law robustness is validated for diverse severe load and system parameter variations. It shows robust performance in terms of high power factor, low total harmonic distortion and output voltage ripples, small overshoot, and short settling time. Index Terms--Digital signal processor (DSP), input/output feedback linearization, neutral point clamped, nonlinear control, pulsewidth modulator (PWM), three-level rectifier, three-phase rectifier.

In this paper, a new control strategy based on the pulsewidth-modulation technique is proposed and applied to an active-current-injection-based unity-power-factor three-phase ac-dc converter. The control circuit of the converter consists of a multivariable inner current regulator and an outer voltage regulator. Based on linear control techniques, the control circuit has been designed. Averaging techniques are used to derive a linear small-signal model of the converter in the frequency domain. Numerical simulations using the Power System Blockset tool of Matlab/Simulink highlight the performance of the proposed control scheme. Experimental results obtained on a 2-kW prototype validate the theoretical approach described in the paper. Index Terms--Averaged small-signal modeling, current injection rectifier, multiple loops linear control design, sequential looping approach, unity power factor.

The steady-state performance of the least mean square adaptive second-order Volterra filter with a zeroth-order term for Gaussian inputs was investigated. To this end, the mean-square-error criterion was analyzed and second-order Volterra least mean square algorithm-based updating equations were derived. Simulation results were found to be in good agreement with theoretical performance predictions, especially for cases involving small step sizes.

A novel fast algorithm for multichannel linear and quadratic adaptive filtering based on the Chandrasekhar equations is proposed. The multichannel linear model makes use of the shift-invariance property and could be described by a time-invariant state-space model to which the Chandrasekhar factorization method is applied. The nonlinear filtering approach, meanwhile, involves the application of the multichannel Chandrasekhar algorithm on the second-order Volterra filter after suitable transformations.

Abstract A novel method of colour image segmentation based on fuzzy homogeneity and data fusion techniques is presented. The general idea of mass function estimation in the Dempster-Shafer evidence theory of the histogram is extended to the homogeneity domain. The fuzzy homogeneity vector is used to determine the fuzzy region in each primitive colour, whereas, the evidence theory is employed to merge different data sources in order to increase the quality of the information and to obtain an optimal segmented image. Segmentation results from the proposed method are validated and the classification accuracy for the test data available is evaluated, and then a comparative study versus existing techniques is presented. The experimental results demonstrate the superiority of introducing the fuzzy homogeneity method in evidence theory for image segmentation.