Robust cerebellar model articulation controller design for unknown nonlinear systems

In this study, a robust cerebellar model articulation controller (RCMAC) is designed for unknown nonlinear systems. The RCMAC is comprised of a cerebellar model articulation controller (CMAC) and a robust controller. The CMAC is utilized to approximate an ideal controller, and the weights of the CMAC are on-line tuned by the derived adaptive law based on the Lyapunov sense. The robust controller is designed to guarantee a specified H/sup /spl infin// robust tracking performance. In the RCMAC design, the sliding-mode control method is utilized to derive the control law, so that the developed control scheme has more robustness against the uncertainty and approximation error. Finally, the proposed RCMAC is applied to control a chaotic circuit. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performance with unknown the controlled system dynamics.     

自适应半耦合稀疏字典学习算法

为实现图像超分辨力重建,提出了一个自适应半耦合稀疏字典学习算法。由于耦合字典学习算法中存在稀疏编码约束条件太过严苛的缺点,本文采用半耦合的字典学习算法。根据在半耦合的字典学习算法中全局字典表达的局限性,分析和采用了多字典训练算法及相应的重建方法。提出了基于自适应图像块聚类算法的半耦合稀疏字典学习算法。仿真实验结果显示,新算法重建得到的Butterfly,Cameraman,Foreman,Plants,Hat和Lena等图像的峰值信噪比(PSNR)分别比用基于K-means聚类算法的半耦合稀疏字典学习算法得到的重建图像高出0.18 dB,0.16 dB,0.52 dB,0.21 dB,0.23 dB和0.14 dB。该算法可以得到更好的图像重建效果。     

Adaptive time-delay controller

A time-delay controller is proposed in this letter. This controller possesses attractive properties, such as requiring only one tuning parameter. Furthermore, it can be applied to higher order systems as well as systems with dominant delay. The basic structure is integrated with an online estimation algorithm to form an adaptive time-delay controller. An experimental study is included to demonstrate its performance and merits compared with two similar controllers.     

Adaptive learning path recommendation model forexamination-oriented education

Adaptive learning paths provide individual learning objectives that best match a learner’s characteristics. This is especially helpful when learners need to balance limited available learning time and multiple learning objectives. The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest. However, most current research only focuses on providing learners with adaptive objects and sequences according to their own interests o...     

Adaptive neural network based controller for robots

A new Adaptive Neural Network (ANN) controller for robot trajectory trackingproblem is developed. A novel and efficient training algorithm for the proposed controller ispresented in this paper. The proposed training algorithm is based on updating the weights of thenetwork each step by minimizing the quadrant tracking errors and their derivatives.A simulation study is carried out on a polar robot manipulator to assure the effectivenessof the proposed trajectory tracking robot control system. The effects of the new controllerparameters and noisy external load disturbances on the control performance are studied. Thesimulation results of the proposed adaptive ANN controller are compared with those of aconventional ANN controller. The obtained results assured the robustness of the proposed ANNcontroller for: (i) uncertainties of the robot arm dynamic model and/or parameters, (ii) variousnoisy external load disturbances. Also, the simulation results assure the effectiveness of theproposed adaptive ANN controller against the conventional ANN one.     

改进的安全协议自适应分析算法

提出一种改进的安全协议自适应分析算法,即修正学习算法La*,解决部分教师缺乏经验的问题,并将字符集扩展为大字符集。对提出的修正学习算法,进行了正确性证明和复杂度分析。该修正学习算法将有助于提高安全协议自适应模型检测的效率、降低分析和设计成本、缓解状态空间爆炸并增强协议本身对环境和各种攻击手段的防御能力。     

Adaptive nonlinear congestion controller for a differentiated-services framework

The growing demand of computer usage requires efficient ways of managing network traffic in order to avoid or at least limit the level of congestion in cases where increases in bandwidth are not desirable or possible. In this paper we developed and analyzed a generic Integrated Dynamic Congestion Control (IDCC) scheme for controlling traffic using information on the status of each queue in the network. The IDCC scheme is designed using nonlinear control theory based on a nonlinear model of the network that is generated using fluid flow considerations. The methodology used is general and independent of technology, as for example TCP/IP or ATM. We assume a differentiated-services network framework and formulate our control strategy in the same spirit as IP DiffServ for three types of services: Premium Service, Ordinary Service, and Best Effort Service. The three differentiated classes of traffic operate at each output port of a router/switch. An IDCC scheme is designed for each output port, and a simple to implement nonlinear controller, with proven performance, is designed and analyzed. Using analysis performance bounds are derived for provable controlled network behavior, as dictated by reference values of the desired or acceptable length of the associated queues. By tightly controlling each output port, the overall network performance is also expected to be tightly controlled. The IDCC methodology has been applied to an ATM network. We use OPNET simulations to demonstrate that the proposed control methodology achieves the desired behavior of the network, and possesses important attributes, as e.g., stable and robust behavior, high utilization with bounded delay and loss, together with good steady-state and transient behavior.     

Discrete iterative learning controller

An effective discrete learning control method is proposed for improving the tracking performance of linear systems repeating the task from cycle to cycle. With this method, the current cycle data are intentionally introduced into the learning control law. Analysis of the convergence is conducted completely in the discrete-time domain. The fast convergence performance is further substantiated by simulation results.<>     

Adaptive robust controller design for multi-link flexible robots

Energy-based robust control strategy was proposed in [12] to improve the control performance of the traditional joint PD control by introducing additional control efforts through the evaluation of vibration related variables. Although the energy-based robust controller always guarantees closed-loop stability, it is not easy to find suitable gains of the terms for a satisfactory control performance. In this paper, adaptive energy-based robust control is presented for both closed-loop stability and automatic tuning of the gains of the additional control terms for desired performance. Simulation results are provided to show the effectiveness of the presented approach.     

Digital repetitive learning controller for three-phase CVCF PWMinverter

In this paper, a plug-in digital repetitive leaning control scheme is proposed for three-phase constant-voltage constant-frequency (CVCF) pulsewidth modulation inverters to achieve high-quality sinusoidal output voltages. In the proposed control scheme, the repetitive controller (RC) is plugged into the stable one-sampling-ahead-preview-controlled three-phase CVCF inverter system using only two voltage sensors. The RC is designed to eliminate periodic disturbance and/or track periodic reference signal with zero tracking error, The design theory of plug-in repetitive learning controller is described systematically and the stability analysis or overall system is discussed. The merits of the controlled systems include features of minimized total harmonic distortion, robustness to parameter uncertainties, fast response, and fewer sensors. Simulation and experimental results are provided to illustrate the effectiveness of the proposed scheme     

自适应单纯太阳能路灯控制器的设计

基于改善季节性负载光伏太阳能路灯运行可靠性的目的,采用新一代自适应单纯太阳能供电路灯控制器设计的方法,通过功率调节,电量检测和剩余电量计算、组网功能等对蓄电池的充、放电以及路灯的开、关、最大功率跟踪等智能控制,提高了太阳能电池的转换效率,延长了蓄电池的使用寿命,降低产品造价。得到了自适应单纯太阳能供电路灯控制器是提高季节性负载光伏太阳能路系统可靠性保证的结论。     

Early Merge mode decision algorithm for 3D-HEVC based on learning model

3D-high efficiency video coding (3D-HEVC) standard is an extension of HEVC.Though 3D-HEVC effectively improves the compression efficiency of 3D video,it also brings huge computational complexity.To greatly reduce the 3D-HEVC coding complexity,an early Merge mode decision approach was proposed.The residual signal that encoded by the Merge mode was firstly extracted as feature information.A learning model was established in terms of the residual signals of the coding unit (CU) in current frame that used early Merge mode as the optimal mode.Finally,the residual signal was extracted for the Merge mode of current CU,and the learning model was used to predict whether the Merge mode was the optimal mode or not.Experimental results show that the proposed early Merge mode decision approach reduces the coding times of 3D-HEVC texture views and depth maps about 41.9% and 24.3%,respectively,and the coding performance degradation is almost negligible.Compared with existing early Merge mode decision approaches,the proposed approach further reduces the coding time,and can be easily integrated into the 3D-HEVC test model due to its design simplicity.     

An online learning fuzzy controller

A fuzzy controller with online learning capability is reported in this paper. The controller learns from a standard proportional plus derivative (PD) controller. It is implicitly assumed that the tuning parameters of the PD controller are already known. The learning is realized via Wang's table lookup scheme. The controllers are applied successfully to control an open-loop unstable system, i.e., the ball and plate system. Experimental studies have demonstrated the performance of the proposed controller.     

改进的“当前”统计模型变采样率目标跟踪算法

为满足实际雷达系统对高精度和高实时性的要求,提出了一种改进的"当前"统计模型变采样率机动目标跟踪算法。该算法针对"当前"统计模型必须预设加速度极值和机动频率的问题,提出一种加速度方差和机动频率在线同步自适应方法,建立改进的"当前"统计模型机动目标跟踪算法;针对在线自适应方法计算量大的问题,结合采样周期的大小与目标机动特性的关系,引入变采样率方法。仿真结果表明,与传统"当前"统计模型相比,改进的"当前"统计模型机动目标跟踪算法能显著提高对不同机动强度目标的跟踪精度;变采样率方法通过减少采样点数,节省了系统资源,提高了跟踪实时性;所提算法将两者结合,用传统的"当前"统计模型1.5~2倍的平均采样周期得到了更小的位置均方根误差,实现了用单模型方法同时改善跟踪精度和实时性的目的。     

Adaptive controller yielding fast convergence rate

A way of improving the transient performance is suggested for a class of model reference adaptive control systems     

Adaptive machine learning algorithm employed statistical signal processing for classification of ECG signal and myoelectric signal

Multidimensional Systems and Signal Processing - In this research paper we present designing and evaluating the electrocardiography (ECG) and Myoelectric signal (EMG) pattern recognition methods...     

半导体激光器恒温控制器算法的实验仿真研究

半导体激光器的输出波长随着温度而变化,同时,输出功率也会随着温度和使用时间的变化而发生漂移,因此,半导体激光器的恒温控制是极其必要的.根据半导体激光器的温度控制特点,对PID和Fuzzy控制进行理论分析,将SIMULINK2.1与FuzzyLogicTool-Box有机地结合,实现了模糊自整定PID参数控制系统的计算机仿真设计.仿真结果表明,此种方法简单,易于实现.此系统还可以应用在其他小型的温控系统中.     

Adaptive resampling algorithm for image zooming

Applying an interpolation function indiscriminately to an image, to resample it, will generally result in aliasing, edge blurring and other artefacts. The authors present an adaptive resampling algorithm for zooming up images. The algorithm is based on analysing the local structure of the image and applying a near optimal and least time-consuming resampling function will preserve edge locations and their contrast. This is done by segmenting the image dynamically into homogeneous areas, as it is scanned or received. Based on the location of the pixel to be computed (whether it is within a homogenous area, is on its edge or is an isolated feature), interpolation, extrapolation or pixel replication is chosen. The algorithm performance, from both a quality and a computational complexity aspect, are compared to different methods and functions previously reported in the literature. The advantage of the algorithm is quite apparent at edges and for large zooming factors     

Adaptive tracking algorithm for mobile communications

The authors propose a new adaptive tracking algorithm which has been shown by computer simulations to outperform the conventional decision feedback equalisation algorithm. Basically, a cost function is formed by generating more than one reference signal based on the time recursive phase characteristic of an MSK signal     

基于概率相关性的AIMM跟踪算法

为解决传统自适应交互式多模型(AIMM)算法计算量普遍过大而不适应于实际工程应用的问题,提出了基于概率相关性的自适应的交互多模型算法(PR-AIMM),该方法在由CA、CV和基于圆周运动的转弯(TR)模型3个基本模型组成的模型集上,利用模型后验概率最近时间相关性自适应地调整马尔可夫转移矩阵的参数,能对空中大部分机动目标进行有效的跟踪,有效地解决在实际工程应用中机动目标跟踪问题.最后运用仿真试验对上述算法进行了合理性和有效性验证,并在实际工程应用中达到了满意的效果.