提高采样控制系统带宽的方法研究

影响控制系统带宽的主要因素有机械结构谐振频率、系统加速能力和系统采样频率。对影响采样控制系统带宽的因素作了详细的分析,给出在低采样频率下扩展系统带宽的两种方法棗虚拟采样法和多频率采样控制法,并对虚拟采样法进行了详细的讨论。实验结果表明,虚拟采样器在插值次数小于等于4时,插值精度可以满足要求,能够将系统带宽扩展3倍左右。     

一种动态模糊管理数字资产对象的方法

探讨了一种动态管理数字资产文件的方法,通过对数字资产的权限属性的模糊判断,提高了网络传输数字资产对象的速度,降低了系统对存贮空间的要求,实验已证明了方法的可行性.     

基于模糊逻辑的异步电动机转速控制

本文设计了基于模糊逻辑控制的速度控制器,以提高异步电动机矢量控制系统对参数变化和负载扰动的鲁棒性,并通过MABLAB/SIMULIINK仿真将其与PI控制的系统速度响应进行比较,仿真结果表明模糊控制能使系统取得较好的控制性能并具有较强的鲁棒性.     

一种模糊自适应控制器

本文提出了一种根据系统输入一输出数据在线改模糊关系的模糊自适应控制器。文中给出一个由不稳定非最小相位线性部分和饱和非线性两部分组成的随机系统仿真示例，仿真结果表明该控制器有效的。     

一种实用的自组织模糊控制器设计方法

介绍一种基于模糊智能比例因子自调整的自组织模糊控制器设计方法 ,给出这种模糊控制器与常规模糊控制器在性能指标上的比较结果 .     

一种基于粗糙集的模糊信息融合方法及应用

将粗糙集理论和模糊逻辑技术结合起来，提出了一种基于粗糙集数据处理的模糊信息融合方法。运用粗糙集的基本理论和简约计算方法，从大量原始数据中发现精简的、概略化的规则，结合模糊逻辑推理建立一致粗糙模糊模型，并提出了对模型进行扩充与完备化的概念。脉动真空灭菌温度控制过程的仿真试验研究结果表明了所提方法的有效性和可行性。     

基于CMAC的模糊神经网络方法

结合模糊神经网络和小脑模糊连接控制CMAC理论，提出训练时间短，精度高的CMAC模糊神经网络方法，给出了网络结构，算法，并通过一个维修经费预测实例讲述了这种算法。     

一种基于模糊PID的立方体机器人控制系统

针对以棱边为支点的立方体机器人坡面自平衡控制问题,提出了一种基于模糊PID的立方体机器人控制系统的设计方法.首先,基于自主设计的一种内部集成的动量轮立方体机器人,通过Lagrange方程建立了坡面上立方体机器人的动力学模型,证明了在平衡点附近是一个不稳定系统且具有能控性和能观性.其次,设计了一种基于模糊PID的角速度-...     

一种模糊故障诊断方法及其应用

在实际的故障检测诊断过程中,由于故障产生原因的多样性和关联性,往往很难做出准确的判断。但是借助于专家的经验的模糊故障诊断方式可以通过专业人士的丰富经验和统计数据对实际的故障情形进行判断,进而做出准确的判断。这种做法综合模糊聚类和模糊模式识别等知识,能够对系统的故障发生时间和类型经行较理想的判别结果。根据以上理论提出模糊故障诊断的聚类-贴近度法,通过实验验证本方法的可行性。     

一种运用遗传算法确定船舶航向控制模糊规则的方法

为了克服船舶航向控制模糊规则确定过程中的盲目性，引入一种与之相适应的遗传算法，以使结果成为某种意义下的最优解，从而从根本上解决这一重要问题．文中运用Matlab的C语言MEX—文件技术，将遗传算法源代码与Matlab直接结合起来，利用后者的强大建模能力，建立起完整的仿真模型．仿真结果表明，船舶的航向控制能力得到明显改善．     

A fuzzy logic based approach to reliability improvement estimation during product development

During early stages of product development process, a vast amount of knowledge and information is generated. However, most of it is subjective (imprecise) in nature and remains unutilized. This paper presents a formal structure for capturing this information and knowledge and utilizing it in reliability improvement estimation. The information is extracted as improvement indices from various design tools, experiments, and design review records and treated as fuzzy numbers or linguistic variables. Fuzzy reasoning method is used to combine and quantify the subjective information to map their impact on product reliability. The crisp output of the fuzzy reasoning process is treated as new evidence and incorporated into a Bayesian framework to update the reliability estimates. A case example is presented to demonstrate the proposed approach.     

An approach of fuzzy logic evaluation and control in SPC

Quality control plays an important part in most industrial systems. Its role in providing relevant and timely data to management for decision‐making purposes is vital. A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. Engineers or managers can evaluate an abnormal process by using SPC zone rules in control charts. In the conventional use of the zone rules the user is only able to determine whether or not the process is out of control. What action should be taken to adjust the process is uncertain and is evaluated based on knowledge of the system and past experiences. This paper explores the integration of fuzzy logic and control charts to create and design a fuzzy–SPC evaluation and control (FSEC) method based on the application of fuzzy logic to the SPC zone rules. A simulation program implementing FSEC was written in Borland C++ 5.0 and simulation results were obtained and analysed. The abnormal processes simulated were automatically adjusted for each of the zone rules tested and showed an improved performance after the control action, thus confirming the merit of the technique as a special method with the specific numerical control action based on a quality evaluation criterion. Copyright © 2000 John Wiley & Sons, Ltd.     

基于层次模糊推理的施工机群动态调度方法研究

以Zadah的模糊数学理论为基础,结合决策融合技术提出了一种基于层次模糊推理的施工机群动态调度方法。与传统的单层模糊调度控制方法相比,该方法具有建模简单且可扩展性好的优点,同时避免了单层推理的维数灾难问题。将该方法应用于施工机群的动态调度系统中,按照生产情况对施工系统进行了建模仿真,仿真结果和调度实例证明了该方法的有效性和合理性。该方法为机群施工以及与之相似的生产施工的调度提供了技术框架。     

An approach based on fuzzy sets for manufacturing system design

The aim of this work is to provide Small and Medium Enterprises (SMEs) with adapted design methods for manufacturing systems. In a total quality strategy, the design activity consists of searching for the means able to satisfy all users' needs from company customers to floor plant workers. That includes usual costs, delays and quality targets but also employees' involvement. Due to the variety of data sources and to the continuous changes of goals, the information is imperfect. The main concerns are uncertainty, inaccuracy and vagueness of both objectives and solutions. The fuzzy set theory provides a quite convenient framework to represent all those imperfections using one mathematical objet. Fuzzy logic is used to aggregate specifications and to deploy solutions. Possibility theory finally allows a graded evaluation of expected performances. The obtained result is graded, providing the engineer with some degrees of freedom for the realization of the manufacturing system. The valuation of the design quality is an undeniable progress towards the continuous improvement of the whole system performance. Finally, such a criterion may also be used for an empirical search for a convenient solution, as for the automatic computation of an optimal result.     

A fuzzy logic approach to an integrated maintenance/production scheduling algorithm

Scheduling algorithms play an important role in manufacturing systems as a means of meeting customer demands. On the other hand, fuzzy logic, which has been successfully implemented in many engineering applications, including the recent work of Vanegas and Labib (2001a,b), has an ability to produce a more gradual transition. This paper presents an algorithm for transforming maintenance data to shop floor information. These shop floor data are then used via a fuzzy-logic based scheduling algorithm to determine optimal production systems control policies. The frequency of breakdowns and the mean number of parts required are used as inputs to the fuzzy logic controller. These inputs are transformed to the mean part arrival rate. The output is then fed to the scheduling algorithm. Finally, the optimal batch size is calculated. The algorithm is demonstrated with simulation.     

Fuzzy logic based approach to FRP retrofit of columns

Current design approaches for seismic retrofit use deterministic variables to describe the geometry, material properties and the applied loads on the bridge column. Using a mechanistic model that considers nonlinear material behavior, these deterministic input variables can be directly mapped to the design parameters. However the results often give a false sense of reliability due to neglecting uncertainties related to the input variables of the analysis (data uncertainty), unpredictable fluctuations of loads and natural variability of material properties, and/or the uncertainty in the analytical model itself (model uncertainty). While methods of reliability analysis can provide a means for designing so as not to exceed specific levels of “acceptable” risk, they do not consider the uncertainty in the assumption of distribution functions for each of the input variables and are built on the basic assumption that the models used perfectly describe reality. This, however, still results in significant unknowns and often design models that are not truly validated across their response space. This paper describes the application of a fuzzy probabilistic approach to capture the inherent uncertainty in such applications. The application of the approach is demonstrated through an example and results are compared to those obtained from conventional deterministic analytical models. It is noted that the confidence in the achieved safety of the retrofit system that is based on the use of the fuzzy probabilistic approach is much higher than that achieved using the deterministic approach. This is due to the consideration of uncertainty in the material parameters as well as the consideration of uncertainty in the assumed crack angle during the design process.     

基于模糊逻辑控制器的WSN移动节点导航算法

为了实现含有移动节点的混合无线传感器网络中的网络覆盖优化,提出了一种点对点的基于模糊逻辑控制器的移动节点导航算法.该算法通过移动节点选择、移动节点向盲区填补坐标径直移动、避让固定节点、避让其他移动节点四个主要步骤,实现移动节点对覆盖盲区的弥补.该算法可以优化网络覆盖,延长网络生命周期,并且在导航过程中兼顾节能与网络负载...     

Evaluation of leagility in supply chains using fuzzy logic approach

In the changing business environment, manufacturing firms can survive by catering to the dynamic demands of the modern customers. Lean principles imply zero inventory and agile principles necessitate safety inventory to tackle volatile market conditions. The leagile paradigm is gaining importance in the contemporary scenario which includes both lean and agile principles. This article presents the conceptual model of leagility imbibed with lean and agile principles. A fuzzy logic approach has been used for the evaluation of leagility in supply chains. This article is used to compute the performance of supply chains using both lean and agile concepts as leagility supply chains using a fuzzy logic approach.     

Adaptive image denoising for speckle noise images based on fuzzy logic

Speckle noise is a kind of ubiquitous noise in medical image, which will damage the texture structure of image and affect the analysis of image structure by doctors. Therefore, we propose an image denoising model based on fuzzy logic, which can eliminate speckle noise in the image well, improve the recognition of the image, and facilitate the acquisition of image information by doctors. The main work arrangement of the algorithm model is to design a membership function that can traverse the noise image and preprocess the noise image to make the image smooth. Then, a mask template of 5 × 5 is designed by the definition of g-l calculus, and there is mainly an unknown parameter in this template. We design the functional relation between this parameter and the image gradient, which makes the model algorithm adaptive. Finally, the convolution operation is performed between the template and the smooth image. By comparison with the existing mainstream models, the overall denoising effect of this model is better than other models, and the relevant numerical indexes are better than other models. This model is an extension of the denoising model of fuzzy theory, which is beneficial to the future research and development.     

A probabilistic alternative to fuzzy logic controllers

Fuzzy logic controllers have been used increasingly in industrial applications. We introduce the probabilistic controller as an alternative to fuzzy logic controllers. The probabilistic controller is a 'universal' controller with a structure closely analogous to a popular type of fuzzy logic controller, but it is not based on fuzzy logic.