The memory state feedback control problem for a class of discrete-time systems with input delay and unknown state delay is addressed based on LMIs and Lyapunov-Krasovskii functional method. Under the action of our des...
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The memory state feedback control problem for a class of discrete-time systems with input delay and unknown state delay is addressed based on LMIs and Lyapunov-Krasovskii functional method. Under the action of our designed adaptive control law, the unknown time-delay parameter is included in memory state feedback controller. Using LMI technique, delay-dependent sufficient conditions for the existence of the feedback controller are obtained. Finally, the effectiveness of the proposed design method is demonstrated by a numerical example.
A control scheme combined with backstepping, radius basis function (RBF) neural networks and adaptive control is proposed for the stabilization of nonlinear system with input and state delay. By using state transforma...
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The wavelet edge detecting method was introduced to the welding seam image processing in this paper, which can make up the defect of usual edge detecting methods in antinoise ability and precise locating ability. The ...
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The wavelet edge detecting method was introduced to the welding seam image processing in this paper, which can make up the defect of usual edge detecting methods in antinoise ability and precise locating ability. The B spline wavelet was applied to extract the image edge and the similarity distance was defined to compare the extracting results. The contrasting results demonstrate the wavelet edge detection is better than the usual methods, which justified the validity that the wavelet transform can be used efficiently in the welding seam image processing.
Object states estimation and data association are main facets of multi-object tracking. Under complex situations, one object often grouped with others, or occluded by other objects or background, which can increase th...
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In the letter [Neurocomputing 71(1–3) (2007) 428–438], there exists one minor error in computing the derivative of V 2 ( ɛ ( t ) ) and thus, the proof of Theorem 1 needs some improvement.
In the letter [Neurocomputing 71(1–3) (2007) 428–438], there exists one minor error in computing the derivative of V 2 ( ɛ ( t ) ) and thus, the proof of Theorem 1 needs some improvement.
A novel color correlogram based particle filter was proposed for an object tracking in visual surveillance. By using the color correlogram as object feature, spatial information is incorporated into object representat...
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The decoupling and linearize (D&L) control of induction motor is an important approach to improve the performance further. The analytical inverse system can realize D&L of nonlinear system when the model is ex...
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The decoupling and linearize (D&L) control of induction motor is an important approach to improve the performance further. The analytical inverse system can realize D&L of nonlinear system when the model is exactly known, but for the induction motor with parameters varying and disturbance, the D&L is destroyed. So the neural network inverse system (NNIS) theory was adapted to approximate the analytical inverse system in order to weaken the couple of rotor flux and speed, the NNIS was designed for the induction motor in the synchronous rotating (dq) reference frame in this paper. Through the analytical inverse system expression we pointed out that the D&L effect is unrelated to the position of d axis. Subsequently, the neural network inverse control (NNIC) structure was proposed. As a special case, the NNIS of induction motor in rotor field oriented (MT) reference frame was also given, the comparison of this NNIC with direct rotor field oriented control (DRFOC) was done and we conclude that it is an improved method of DRFOC. At last, the simulation and experiment were done to test the proposed structures.
Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is ro...
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Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is robust to partial occlusion, is rotation and scale invariant and computationally efficient. Particle filter has been proven very successful for non-linear and non-Gaussian estimation tracking problems. The article presents the integration of color distributions into particle filtering. A target is tracked with a particle filter by comparing its histogram with the histograms of the sample positions using the Bhattacharyya distance. Additionally, to solve the sample impoverishment (all particles collapse to a single point within a few iterations) in the particle-filter algorithm, a new resampling algorithm is proposed to tackle sample impoverishment. The performance of the proposed filter is evaluated qualitatively on various real-world video sequences. The experimental results show that the improved color-based particle filter algorithm can reduce sample impoverishment effectively and track the moving object very well.
In view of the bad forecasting results of the standard epsiv-support vector machine (SVM) for product sale series with the normal distribution noise, a SVM based on the Gaussian loss function named by g-SVM is propose...
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In view of the bad forecasting results of the standard epsiv-support vector machine (SVM) for product sale series with the normal distribution noise, a SVM based on the Gaussian loss function named by g-SVM is proposed. And then, a hybrid forecasting model for product sales and its parameter-choosing algorithm are presented. The results of its application to car sale forecasting indicate that the short-term forecasting method based on g-SVM is effective and feasible.
Aiming at the product demand series with multidimension, small samples, nonlinearity and multi-apex in manufacturing enterprise, chaos theory is combined with support vector machine, and a kind of chaotic support vect...
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Aiming at the product demand series with multidimension, small samples, nonlinearity and multi-apex in manufacturing enterprise, chaos theory is combined with support vector machine, and a kind of chaotic support vector machine named C v -SVM is proposed. And then, a product demand forecasting method and its relevant parameter-choosing algorithm are put forward. The results of application in car demand forecasting show that the forecasting method based on C v -SVM is effective and feasible.
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