a fuzzy-based method for evaluating the trustworthiness of software processes

University of Paderborn; Fraunhofer Inst. Exp. Softw. Eng. (IESE); Chinese Academy of Science (ISCAS)

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Spatial relations, reflecting the complex association between geographical phenomena and environments, are very important in the solution of geographical issues. Different spatial relations can be expressed by indicators which are useful for the analysis of geographical issues. Urbanization, an important geographical issue, is considered in this paper. The spatial relationship indicators concerning urbanization are expressed with a decision table. Thereafter, the spatial relationship indicator rules are extracted based on the application of rough set theory. The extraction process of spatial relationship indicator rules is illustrated with data from the urban and rural areas of Shenzhen and Hong Kong, located in the Pearl River Delta. Land use vector data of 1995 and 2000 are used. The extracted spatial relationship indicator rules of 1995 are used to identify the urban and rural areas in Zhongshan, Zhuhai and Macao. The identification accuracy is approximately 96.3%. Similar procedures are used to extract the spatial relationship indicator rules of 2000 for the urban and rural areas in Zhongshan, Zhuhai and Macao. An identification accuracy of about 83.6% is obtained.

Prediction of marine sediment corrosion by fuzzy clustering analysis

Along with the development of marine industries, especially marine petroleum exploitation, more and more pipelines are buried in the marine sediment. It is necessary and useful to know the corrosion environment and corrosiveness of marine sediment. In this paper, field corrosion environmental factors were investigated in Liaodong Bay marine sediment containing sulfate-reducing bacteria (SRB) and corrosion rate of steel in the partly sediment specimens were determined by the transplanting burying method. Based on the data, the fuzzy clustering analysis (FCA) was applied to evaluate and predict the corrosiveness of marine sediment. On that basis, the influence factors of corrosion damage were discussed.

An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

研磨抛光机器人运动控制器设计与研究

本文设计了研磨抛光机器人运动控制器的核心硬件结构和软件模块,采用了参数模糊自整定PID机器人关节位置控制策略,通过实验表明该运动控制器可以大大降低研磨抛光机器人的位置跟踪误差。建立的模块化的软件体系,便于运动控制器的维护和扩展,并可将其应用到其它工业机器人上。

基于ARM的研磨抛光机器人运动控制器的设计

本文设计了研磨抛光机器人分布式控制系统中的一种运动控制器,并对运动控制器基于AT91M40800微控制器的硬件结构、基于μC/OS-Ⅱ实时操作系统的软件模块和采用的参数模糊自整定PID机器人关节位置控制策略进行了详细介绍。实验表明该控制器可以大大降低研磨抛光机器人的位置跟踪误差。提高了关节控制的计算及处理能力,易于扩展和维护。

一种改进的模糊PD控制器

充分利用非线性跟踪微分器获得高质量微分信号的特性,将跟踪微分器与传统的简单模糊PD控制器相结合,提出一种简单的高性能的改进的模糊PD控制器.该改进模糊控制器的最显著特点是对测量噪声的强鲁棒性和工程易实现性.数值仿真证明了其有效性和高效性.

最优Fuzzy—GA PID控制器及其应用

提出了一种基于模糊推理与遗传算法的最优PID控制器的设计方法，该控制器由离线和在线2部分组成，在离线部分，以系统响应的超调量、上升时间及调速时间为性能指标，利用遗传算法搜索出一组最优的PID参数Kp^*，Ti^*及Td^*，为在线部分调节的初始值，在在线部分，采用一个专用的PID参数优化程序，以离线部分获得的Kp^*,Ti^*及Td^*为基础，根据系统当前的误差e和误差变化率·↑e，通过模糊推理在线调整系统瞬态响应的PID参数，以确保系统的响应具有最优的动态和稳态性能，计算机仿真结果表明，与传统的PID控制器相比，这种最优PID控制器具有良好的控制性能和鲁棒性能，可用于控制不同的对象和过程。

自适应模糊PID控制在AUV控制中的应用

自治潜水器(AUV,Autonomous Underwater Vehicle)是非线性、强耦合、大惯性的多输入多输出系统,又由于受到海流、传感器、执行机构等不确定性的影响,对AUV控制器的鲁棒性能提出了更高的要求。本文针对我国正在研制开发的长航程自治潜水器的特性及其对航行控制的要求,将PID控制与模糊控制的简便性、灵活性以及鲁棒性结合起来,为AUV设计了可在线修改PID参数的自适应模糊PID控制器,仿真结果证明了该种控制方法不但提高了AUV系统的动态特性,而且可在参数摄动和外界扰动时获得较好的控制性能。

基于模糊神经网络水下机器人直接自适应控制

提出了基于广义动态模糊神经网络的水下机器人直接自适戍控制方法,该控制方法既不需要预先知道模糊神经结构,也不需要预先的训练阶段,完全通过在线自适应学习算法构建水下机器人的逆动力学模型.首先,本文提出了基于这种网络结构的水下机器人直接自适应控制器,然后,利用Lyapunov稳定理论,证明了基于该控制器的水下机器人控制系统闭环稳定性,最后,采用某水下机器人模型仿真验证了该控制方法的有效性。

模糊自适应PID控制在载人潜器动力定位中的应用研究

根据我国正在研制开发的某型载人潜器的特性及其对动力定位的要求 ,设计了一个模糊自适应PID控制器 ,通过模糊推理实现在线修改PID参数 ,仿真结果证明了这种方法具有良好的效果和应用性。

基于模糊神经网络的多移动机器人自学习协调系统

研究多移动机器人的运动规划问题．针对机器人模型未知或不精确以及环境的动态变化，提出一种自学习模糊控制器（ＦＬＣ）来进行准确的速度跟踪．首先通过神经网络的学习训练构造ＦＬＣ，再由再励学习算法来在线调节ＦＬＣ的输出，以校正机器人运动状态，实现安全协调避撞

Fuzzy层次神经网络

本文提出和归纳了Ｆｕｚｚｙ前馈神经网络模型，并提出了Ｆｕｚｚｙ前馈神经网络的反向传播学习算法，模拟结果表明利用ＦｕｚｚｙＢＰ学习算法训练的Ｆｕｚｚｙ前馈神经网络具有较好的非逻辑归纳能力和Ｆｕｚｚｙ规则表达能力。

模糊自适应PID在磁悬浮系统中的应用

针对EMS型磁悬浮列车悬浮系统的非线性、迟滞性及模型不确定的特点,本文采用了模糊自适应整定PID控制技术来满足其对动态和静态性能的要求。仿真结果表明模糊自适应整定PID控制器学习精度高、收敛速度快、在系统同时存在磁悬浮系统参数的变化和负载扰动时.具有较强的鲁棒性和抗干扰能力。