自适应超混沌系统修正广义同步及其电路实现

运用自适应控制方法,实现超混沌Chen系统的同结构投影同步,并能辨识出响应系统的未知参数。首先通过采用MATLAB进行数字仿真,表明该自适应控制方法的可行性以及系统的参数辨识作用。其次利用EWB仿真设计模拟电路,结果与MATLAB的仿真结果相吻合,从而进一步证明了该自适应控制方法的有效性和系统参数辨识方法。     

基于自适应灰色模型的路面使用性能预测

在灰色理论建模的基础上,通过采用预测的最新数据替代最老数据这种方式,建立了自适应灰色模型,并将其应用于路面使用性能预测。能够克服缺少历史数据的不足,同时,与以往的预测模型相比,更加准确,更加符合路面使用性能的变化规律。通过MATLAB编程实现,使得预测更加简便易行。     

医用充气式保温毯控制系统设计及仿真

针对医用充气式保温毯的温度控制要求,提出其温度控制闭环系统的结构,系统的硬件电路主要由温度传感器、单片机及RS232通讯模块等组成。采用PWM方法控制晶闸管导通时间的方式来改变加热丝的加热时间、采用自适应模糊PID控制方法实现保温毯气囊出气温度的自动调节与控制。将整个控制过程分为两个阶段:自动调节初始阶段,加热丝加热的占空比为100%;当温度达到一定值后转入自适应模糊PID控制阶段,由相应控制量来决定加热丝加热的占空比,实现气体温度的调节。通过MATLAB仿真分析表明,所采用的自适应模糊PID控制方法优于常规的PID控制,具有良好的适应性和鲁棒性,可明显提高系统的稳态精度。     

Adaptive fuzzy tracking control of nonlinear time-delay systems with unknown virtual control coefficients

In this paper, a novel adaptive fuzzy control scheme is proposed for a class of uncertain single-input and single-output (SISO) nonlinear time-delay systems with the lower triangular form. Fuzzy logic systems are used to approximate unknown nonlinear functions, then the adaptive fuzzy tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the backstepping approach. The proposed controller guarantees uniform ultimate boundedness of all the signals in the closed-loop system, while the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters is not more than the order of the systems under consideration. Finally, simulation studies are given to demonstrate the effectiveness of the proposed design scheme.     

Distributing test cases more evenly in adaptive random testing

Adaptive random testing (ART) has recently been proposed to enhance the failure-detection capability of random testing. In ART, test cases are not only randomly generated, but also evenly spread over the input domain. Various ART algorithms have been developed to evenly spread test cases in different ways. Previous studies have shown that some ART algorithms prefer to select test cases from the edge part of the input domain rather than from the centre part, that is, inputs do not have equal chance to be selected as test cases. Since we do not know where the failure-causing inputs are prior to testing, it is not desirable for inputs to have different chances of being selected as test cases. Therefore, in this paper, we investigate how to enhance some ART algorithms by offsetting the edge preference, and propose a new family of ART algorithms. A series of simulations have been conducted and it is shown that these new algorithms not only select test cases more evenly, but also have better failure detection capabilities.     

Simplified adaptive estimation

A simplified adaptive scheme is suggested for the estimation of the state vector of linear systems driven by white process noise that is added to an unknown deterministic signal. The design approach is based on embedding the Kalman filter (KF) within a simplified adaptive control loop that is driven by the innovation process. The simplified adaptive loop is idle during steady-state phases that involve white driving noise only. However, when the deterministic signal is added to the driving noise signal, the simplified adaptive control loop enhances the KF gains and helps in reducing the resulting transients. The stability of the overall estimation scheme is established under strictly passive conditions of a related system. The suggested method is applied to the target acceleration estimation problem in a Theater Missile Defence scenario.     

Optimally Adaptive Integration of Univariate Lipschitz Functions

We consider the problem of approximately integrating a Lipschitz function f (with a known Lipschitz constant) over an interval. The goal is to achieve an additive error of at most ε using as few samples of f as possible. We use the adaptive framework: on all problem instances an adaptive algorithm should perform almost as well as the best possible algorithm tuned for the particular problem instance. We distinguish between and , the performances of the best possible deterministic and randomized algorithms, respectively. We give a deterministic algorithm that uses samples and show that an asymptotically better algorithm is impossible. However, any deterministic algorithm requires samples on some problem instance. By combining a deterministic adaptive algorithm and Monte Carlo sampling with variance reduction, we give an algorithm that uses at most samples. We also show that any algorithm requires samples in expectation on some problem instance (f,ε), which proves that our algorithm is optimal.     

Continuous collision detection for adaptive simulation of articulated bodies

We perform continuous collision detection (CCD) for articulated bodies where motion is governed by an adaptive dynamics simulation. Our algorithm is based on a novel hierarchical set of transforms that represent the kinematics of an articulated body recursively, as described by an assembly tree. The performance of our CCD algorithm significantly improves as the number of active degrees of freedom in the simulation decreases.     

Using adaptive e-news to improve undergraduate programming courses with hybrid format

With the growing popularity of computers and the Internet, most teachers are taking advantage of Internet functions to assist in both teaching and student learning. However, students only login to the assisted learning system once or twice a week on average to surf for relevant references, participate in discussions, or hand in assignments, even though information and interaction are greatly enhanced by these online services. Therefore, increasing the transfer rate of useful information and interaction during the course period would help students learn. To this end, we designed an e-news delivery system that sends personalized e-mails to every student’s mailbox to make information access and peer interaction more convenient and efficient. Through the daily habit of using e-mail, students can receive the latest information, new instructions or peer discussions early. To encourage usage of the system, a user friendly interface was designed. The e-news system takes advantage of webpage hyperlinks to allow students to click on interesting topics while reading e-news; doing so will cause another window to pop-up with detailed content retrieved from the web-based learning system. A mouse-click allows students to login to the web-based learning system and extract the details of the chosen topic. To enhance the efficiency of e-news, learners’ needs and interests were taken into consideration when forming the e-news content. Students’ learning status and reading preferences are assessed to determine which information or content to include in the e-news. Experiment results reveal that e-news and personalized information greatly promote the reading of new information and participation in online activities.     

Mining e-Learning domain concept map from academic articles

Recent researches have demonstrated the importance of concept map and its versatile applications especially in e-Learning. For example, while designing adaptive learning materials, designers need to refer to the concept map of a subject domain. Moreover, concept maps can show the whole picture and core knowledge about a subject domain. Research from literature also suggests that graphical representation of domain knowledge can reduce the problems of information overload and learning disorientation for learners. However, construction of concept maps typically relied upon domain experts in the past; it is a time consuming and high cost task. Concept maps creation for emerging new domains such as e-Learning is even more challenging due to its ongoing development nature. The aim of this paper is to construct e-Learning domain concept maps from academic articles. We adopt some relevant journal articles and conference papers in e-Learning domain as data sources, and apply text-mining techniques to automatically construct concept maps for e-Learning domain. The constructed concept maps can provide a useful reference for researchers, who are new to the e-Leaning field, to study related issues, for teachers to design adaptive learning materials, and for learners to understand the whole picture of e-Learning domain knowledge.