基于综合型模糊支持向量机的故障诊断方法及应用

设备信息和故障的不确定性、模糊性及故障样本的缺乏给故障诊断带来了较大的困难.针对该问题,分析了现有模糊支持向量机的原理和优缺点,提出了一种综合型模糊支持向量机.该模糊支持向量机既可以处理样本含有模糊信息的情况,又可以解决支持向量机分类中存在的不可分问题.然后,提出了基于综合型模糊支持向量机的故障诊断方法,并在某电路系统故障诊断中开展了应用研究.应用结果表明,该诊断方法在设备状态存在模糊性和故障样本较少的情况下,与现有模糊支持向量机诊断方法相比,实现了较准确的故障诊断.     

基于支持向量机的模糊稳健优化设计方法

在模糊稳健设计中,需要采用随机模拟方法计算模糊概率和非线性约束函数,但计算效率很低.为此,提出了一种基于支持向量机的模糊稳健设计方法.采用支持向量回归机对模糊概率进行仿真计算,采用支持向量回归机或分类机作为非线性约束函数的替代模型,显著降低了模糊稳健优化设计的机时消耗.给出了新方法的具体算法步骤,并通过模糊稳健优化设计...     

非线性控制系统的支持向量机辨识建模研究

针对非线性控制系统辨识建模难的问题,系统研究了基于支持向量机的非线性控制系统的辨识建模理论和方法,然后利用回归支持向量机(Support Vector Regression,SVR)设计了一个非线性控制系统的辨识建模系统.仿真试验结果表明,SVR具有很高的建模精度和较强的泛化能力,从而验证了该辨识方法的有效性和先进性.     

一种新的机电设备状态趋势智能混合预测模型

针对机电设备运行状态受多因素影响,变化趋势复杂,难以用单一预测方法进行有效预测的问题,提出一种新的基于改进灰色系统一支持向量机一神经模糊系统的智能混合预测模型。该模型首先利用改进灰色系统弱化数据序列波动性、支持向量机处理小样本和模糊神经系统处理非线性模糊信息的优点,分别进行趋势预测,然后通过改进遗传算法对这三者的预测结果进行自适应加权组合。将该模型应用于信号随机波动性较强、趋势变化复杂的标准算例和某机组振动趋势的预测中,研究结果表明,该模型的预测性能均优于上述三种单一预测方法。     

基于虚拟仪器和模糊SVM的航空发动机润滑系统故障诊断方法

利用虚拟仪器技术实现基于模糊支持向量机的航空发动机润滑系统故障诊断。通过对润滑油油液中磨损元素进行光谱识别与分析,提取故障诊断所需的特征参数作为支持向量机的学习样本,使用ActiveX技术在LabVIEW中调用MATLAB.m文件,完成对润滑系统的故障诊断,并比较了基于BP神经网络的诊断方法与模糊SVM故障诊断方法的诊断结果。结果表明:模糊SVM方法在故障诊断速度和诊断准确性方面都具有明显优势,其平均故障识别率达到95%以上。     

基于遗传算法的双模糊控制器设计及应用

对非线性、时变性的不确定系统,模糊控制是一种较有效的方法.但模糊控制器隶属度函数及模糊推理规则的合适选取比较困难.将遗传算法引入到双模糊控制器的设计中,对倒立摆系统的控制仿真结果表明,该方法是有效的.     

基于遗传算法的双模糊控制器设计及应用

对非线性、时变性的不确定系统,模糊控制是一种较有效的方法.但模糊控制器隶属度函数及模糊推理规则的合适选取比较困难.将遗传算法引入到双模糊控制器的设计中,对倒立摆系统的控制仿真结果表明,该方法是有效的.     

基于支持向量机的汽车液压离合器的故障诊断

为了能够提高汽车液压离合器的故障诊断效率和精度,利用自适应模糊支持向量机邻近增量算法在汽车液压离合器故障诊断中应用进行了研究。分析了汽车液压离合器的常见故障;建立了基于模糊支持向量机的故障诊断模型;研究了邻近增量算法的基本原理;最后,经过仿真分析,验证了该算法的有效性,表明该故障诊断方法具有较高的鲁棒性。     

基于鲁棒支持向量机的非线性系统辨识

支持向量机(SVM)是一种基于结构风险最小化原理的学习技术,该文利用鲁棒支持向量机对非线性系统进行黑箱建模,首先推导出鲁棒支持向量机的基本理论,给出了对偶优化问题,并结合一个具体的例子进行了仿真实验,结果验证了所提出的方法的正确性和有效性.     

基于支持向量机的集装箱起重机建模与辨识

对岸桥集装箱起重机的相关研究中,高度非线性的系统建模是一个难点,传统辨识方法难以较好的描述其系统特性。支持向量机方法具有较强的非线性回归能力,精确建立系统的模型,经过仿真可以看到支持向量机对起重机模型较好的辨识作用,基于此模型的P ID控制也收到了较好的控制效果。     

Fuzzy knowledge-based approach to treating uncertainty in inventory control

Inventory control in complex manufacturing environments encounters various sources of uncertainty and imprecision. This paper presents one fuzzy knowledge-based approach to solving the problem of order quantity determination, in the presence of uncertain demand, lead time and actual inventory level. Uncertain data are represented by fuzzy numbers, and vaguely defined relations between them are modelled by fuzzy if-then rules. The proposed representation and inference mechanism are verified using a large number of examples. The results of three representative cases are summarized. Finally, a comparison between the developed fuzzy knowledge-based and traditional, probabilistic approaches is discussed.     

A fuzzy logic-based approach in capacity-planning problems

In capacity-planning systems, various sources of uncertainty and imprecision are encountered. In most cases, the uncertainty is determined by the subjective beliefs of managers linguistically. However, the measurement of mangers’ judgments is difficult and vague. Therefore, a fuzzy logic-based approach is proposed to deal with capacity-planning problems in the presence of the uncertain demand, set-up resources, and the capacity constraints. Firstly, fuzzy numbers are used to represent uncertain data. Secondly, fuzzy if-then rules are employed to model vaguely defined relations between fuzzy numbers. Then, the computational aspects of fuzzy models and interpretations of inference results are illustrated by a numerical case. Finally, three examples are used to verify the proposed representation and inference mechanism.     

最小二乘支持向量机在空调温控系统中的应用

温度控制系统是空调控制系统的核心,传统的温控系统采用PID调节和普通模糊控制。利用最小二乘支持向量机（Least Squares SVM,LS-SVM）理论,建立模糊控制器,通过与一般模糊控制仿真实验结果进行比较,验证了其在温控系统的有效性。     

自适应神经模糊推理系统在倒立摆控制中的应用

针对单级倒立摆系统具有多变量、非线性、绝对不稳定的特点,应用Matlab/Simulink设计了用于倒立摆系统的、基于自适应神经模糊推理系统的ANFIS控制器,采用反向传播算法和最小二乘算法的混合算法对倒立摆控制样本数据进行学习,调整各变量的隶属度函数,自动产生模糊规则.仿真结果表明,ANFIS控制器对倒立摆系统的摆杆角度和小车位置的控制过程具有良好的动态性能和稳态性能.     

Grasping force controller for parallel grip with fuzzy rules emulated networks

A grasping force regulation for industrial parallel grips is developed without any requirement of mathematic model regarding to the contact mechanism and system dynamic. The physical system including the grasping dynamic and contact mechanism is considered as a class of unknown nonlinear discrete-time systems. An adaptive network called multi-input fuzzy rules emulated network (MiFREN) is implemented as the controller. This control scheme is performed by if-then rules which can be directly defined by human knowledge regarding to the gripper’s specification and objects. The learning algorithm based on gradient search is developed to tune all adjustable parameters inside MiFREN. The system performance and stability can be guaranteed by the time-varying learning rate. An industrial parallel grip SCHUNK-WSG 50 with the proposed controller demonstrates the performance via the experimental setup. Furthermore, the performance can be spontaneously improved within the next iteration of the learning process.     

A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence, pattern recognition is very useful in identifying the process problems. In this study, a multiclass SVM (SVM) based classifier is proposed because of the promising generalization capability of support vector machines. In the proposed method type-2 fuzzy c-means (T2FCM) clustering algorithm is used to make a SVM system more effective. The fuzzy support vector machine classifier suggested in this paper is composed of three main sub-networks: fuzzy classifier sub-network, SVM sub-network and optimization sub-network. In SVM training, the hyper-parameters plays a very important role in its recognition accuracy. Therefore, cuckoo optimization algorithm (COA) is proposed for selecting appropriate parameters of the classifier. Simulation results showed that the proposed system has very high recognition accuracy.     

污土铣刨收集机械切深模糊控制策略研究

根据污土铣刨收集机械中螺旋滚筒液压悬挂系统具有非线性、时变性和外负载干扰的特点,提出污土铣刨收集机械螺旋滚筒悬挂系统模糊PID控制的设计方法,建立模糊推理系统,并利用Matlab/Simulink软件对悬挂系统切深进行模糊PID复合控制和PID控制仿真,研究结果表明,采用模糊PID复合控制策略比PID控制更能适用于螺旋滚筒悬挂系统.     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.     

基于SVM的船舶动力定位系统预测控制

支持向量机(SVM)是基于统计学习理论的新一代机器学习技术。基于预测控制思想,利用支持向量机回归进行非线性系统辨识,并将支持向量机模型应用到船舶动力定位(DP)预测控制,提出一种基于支持向量机的非线性系统预测控制策略。仿真实验表明,支持向量机在小样本情况下具有良好的非线性建模能力和泛化能力,预测控制效果良好。     

Self-organizing fuzzy control of constant cutting force in turning

Constant force control is gradually becoming an important technique in the modern manufacturing process. Especially, constant cutting force control is a useful approach in increasing the metal removal rate and the tool life for turning systems. However, turning systems generally have nonlinear with uncertainty dynamic characteristics. Designing a model-based controller for constant cutting force control is difficult because an accurate mathematical model in the turning system is hard to establish. Hence, this study employed a model-free fuzzy controller to control the turning system in order to achieve constant cutting force control. Nevertheless, the design of the traditional fuzzy controller (TFC) presents difficulties in finding control rules and selecting an appropriate membership function. Moreover, the database and fuzzy rules of a TFC are fixed after the design step and then cannot appropriately regulate ones real time according to the system output response and the desired control performance. To solve the above problem, this work develops a self-organizing fuzzy controller (SOFC) for constant cutting force control to evaluate control performance of the turning system. The SOFC continually updates the learning strategy in the form of fuzzy rules, during the turning process. The fuzzy rule table of this SOFC can be begun with zero initial fuzzy rules which not only overcome the difficulty in the TFC design, but also establish a suitable fuzzy rules table, and support practically convenient fuzzy controller applications in turning systems control. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old lathe for a turning system to evaluate the feasibility of constant cutting force control. The SOFC has a better control performance in constant cutting force control than does the TFC, as verified in experimental results.