发电机组的一种多模型自学习控制

针对发电机组励磁与汽门的综合控制,研究了一种多模型自学习控制(MMSC).首先,建立机组不同工况下的样本数据并归纳模糊控制器(FLC)规则,随后采用模糊聚类算法将样本约简为典型工况,并得到对应于典型工况的模型库与控制器库.MMSC的控制量为多个FLC输出的加权集成,而加权系数由模型匹配程度决定.采用学习能力强的支持向量机来实现FLC的自学习和在线优化.仿真实验验证了MMSC的控制性能和效果.     

感应电机矢量控制系统的SVM—FIS自学习控制

研究交流电机系统特性,针对常规模糊推理系统自学习能力不强,用支持向量机与模糊系统结合,提出了一种支持向量机-模糊推理系统,由支持向量机实现模糊推理系统的自学习.设计一种基于自适应混沌优化算法的支持向量机-模糊推理自学习控制器,并将其应用于无速度传感器感应电机矢量控制系统的速度控制.控制系统采用定转子自适应磁通观测器估计磁通,用转速动态估计器来估算转子转速.仿真结果表明,控制方法响应速度快,鲁棒性强,稳定性好,是一种有效的控制方法.     

滞后复杂系统的多步预报自学习模糊控制

针对具有纯时滞及非线性的复杂系统，提出一种基于多步预报的自学习模糊控制算法，使控制效果基本不依赖于初始控制表，而控制表可以在线修正，以满足系统快速性和稳态特性的要求。仿真结果令人满意。     

自学习模糊智能洗衣机的一种设计方案

本文介绍了一种具有自学习功能的模糊智能洗衣机的设计方案，它是以单片机MC86HC05SR3为核心，实现洗衣机的智能控制，提高洗衣质量。文中还介绍了系统的软硬件组成并较详细介绍了采用动态监视的方法实现洗衣机自学习的功能。     

基于遗传算法的模糊支持向量网络控制

将模糊控制与支持向量网络相结合，设计了一种模糊支持向量网络控制器．该控制器融合了模糊控制与支持向量网络的优点，具有不依赖被控对象模型、泛化能力强等特点．利用遗传算法来优化支持向量机参数和控制器比例因子参数，以期实现最优的控制性能. 仿真结果表明了控制系统具有优良的控制性能．     

滞后复杂系统的多步预报自学习模糊控制

针对具有纯时滞及非线性的复杂系统，提出一种基于多步预报的自学习模糊控制算法，使控制效果基本不依赖于初始控制表，而控制表可以在线修正，以满足系统快速性和稳态特性的要求。仿真结果令人满意。     

非线性系统的多神经网络自学习控制

本文提出了一种未知非线性动力学系统的多网络自学习控制方法。通过对系统的神经元网络辨识器和神经元网络控制器的有机结合，发展了基于逆动力学辨识器的控制网络广义Ｄｅｌｔａ学习规则，从而使得整个控制系统具有很强的自、自学习能力。文中最后通过对系统进行的仿真研究证实了这种控制结构的有效性，仿真例子说明经过１００个周期学习后，其系统的跟踪误差控制在１％以内。     

混沌系统的一种自学习模糊控制

提出一种基于遗传算法的自学习模糊控制方法控制混沌系统。用一种改进的遗传算法学习模糊控制器的隶属度函数，以改善模糊控制器的性能，使其达到良好的控制效果。用此方法控制Ｈｅｎｏｎ系统的混沌行为，效果良好。     

浅谈基于Stateflow的自学习模型

随着汽车电子产业的快速发展,汽车产品应用电子控制技术成为发展的必然趋势,如何实现智能化电子控制系统是本文研究的主要方向。本文采用Stateflow实现精度控制,达到系统建模自学习的功能,缩短开发周期实现优化目的。     

用继电自整定实现模糊PID智能控制

从提高控制器的智能化水平出发，文中提出了模糊PID自适应控制与继电自整定相结合构成PID双模智能控制器的方法。即用继电自整定法整定出PID控制的初始参数，然后切换到模糊PID自适应控制，完成模糊PID智能控制。将该算法应用于一温控系统中，得到了令人满意的效果。     

Nonlinear variable structure excitation and steam valving controllers for power system stability

A set of novel nonlinear variable structure excitation and steam-valving controllers are proposed in this paper. On the basis of the classical dynamic equations of a generator, excitation control and steam valving control are simultaneously considered. Design of these controllers combines the differential geometry theory with the variable structure controlling theory. The mathematical model in the form of ＂an affine nonlinear system＂ is set up for the control design of a large-scale power plant. The dynamic performance of the nonlinear variable structure controllers proposed for a single machine connected to an infinite bus power system is simulated. Simulation results show that the nonlinear variable structure excitation and steam-valving controllers give satisfactory dynamic performance and good robustness.     

模糊支持向量分类机

研究了当训练点的输出为模糊数时支持向量分类机的构建问题。对于线性模糊分类问题,首先将其转化为模糊系数规划。利用模糊系数规划的λ-最优规划,求解模糊系数规划得到模糊最优解（模糊集合）以及模糊最优分类函数集（取值为最优分类函数而隶属度为λ（0≤λ≤1）的模糊集合）,从而构造线性模糊支持向量分类机。对于非线性模糊分类问题,引入核函数,类似干线性模糊分类问题得到非线性模糊支持向量分类机。最后构造显示模糊支持向量分类机特点的模糊支持向量集（取值为模糊训练点,隶属度为λ（0≤λ≤1）的模糊集合）。模糊支持向量分类机较好地解决了支持向量机中含有模糊信息的分类问题。     

Fuzzy functions with support vector machines

A new fuzzy system modeling (FSM) approach that identifies the fuzzy functions using support vector machines (SVM) is proposed. This new approach is structurally different from the fuzzy rule base approaches and fuzzy regression methods. It is a new alternate version of the earlier FSM with fuzzy functions approaches. SVM is applied to determine the support vectors for each fuzzy cluster obtained by fuzzy c-means (FCM) clustering algorithm. Original input variables, the membership values obtained from the FCM together with their transformations form a new augmented set of input variables. The performance of the proposed system modeling approach is compared to previous fuzzy functions approaches, standard SVM, LSE methods using an artificial sparse dataset and a real-life non-sparse dataset. The results indicate that the proposed fuzzy functions with support vector machines approach is a feasible and stable method for regression problems and results in higher performances than the classical statistical methods.     

深空探测航天器姿态控制系统故障定位

执行机构与敏感器故障检测与定位是深空探测任务卫星平台可靠运行的前提和保障.本文从数据的角度出发,结合姿控系统工作机理,提出一种基于神经网络和支持向量机结合的故障诊断方法用于检测并定位故障.故障诊断方法分为3步,首先采集姿控系统的状态信息,采用神经网络对闭环姿控系统中未知动态特性建模并进行预测;然后将姿控系统敏感器信号与神经网络预测输出比较生成残差并提取故障特征;最后采用支持向量机辨识残差特征检测故障,并结合运动学特性分析定位故障.仿真结果表明本文所提方法可以有效提取、辨识故障特征,实现执行器与敏感器的故障检测定位.     

Fuzzy—PID复合控制

介绍自动控制理论的发展历程,阐述由模糊控制和PID控制构成新型智能控制对复杂系统进行效控制的途径,说明了模糊控制器的设计过程。     

Multi-model predictive control for wind turbine operation under meandering wake of upstream turbines

In wind farm operation, the performance and loads of downstream turbines are heavily influenced by the wake of the upstream turbines. Furthermore, the actual wake is more challenging due to the dynamic phenomenon of wake meandering, i.e. the turbine wake often demonstrates dynamic shift over time. To deal with the time-varying characteristics of wake meandering, a multiple model predictive control (MMPC) scheme is applied to the individual pitch control (IPC) based load reduction. The coherence function in the spectral method is used to generate the stochastic wind profile including wake meandering at upstream turbine, and a simplified wake meandering model is developed to emulate the trajectory of the wake center at downstream turbine. The Larsen wake model and Gaussian distribution of wake deficit are applied for composing wind profiles across the rotor of downstream turbines. A set of MMPC controllers are designed based on different linearized state-space models, and are applied in a smooth switching manner. Simulation results show significant reduction in the variation of both rotor speed and blade-root flapwise bending moment using the MMPC based IPC by including the wake meandering, as compared to a benchmark PI controller designed by NREL.     

一种确定高斯核模型参数的新方法

支持向量机中核函数及其参数的选择非常重要,该文提出了一种利用支持向量之间的距离求取高斯核函数参数的有效方法。该方法充分利用了支持向量机方法的最优判别函数仅仅与支持向量有关,并且支持向量为高斯核中心的特点。实验结果表明,该方法较好地反映了图像特征的本质,解决了高斯核函数参数在实际使用中不易确定的问题。     

Cycle-breaking acceleration for support vector regression

Support vector regression (SVR) is a powerful tool in modeling and prediction tasks with widespread application in many areas. The most representative algorithms to train SVR models are Shevade et al.'s Modification 2 and Lin's WSS1 and WSS2 methods in the LIBSVM library. Both are variants of standard SMO in which the updating pairs selected are those that most violate the Karush-Kuhn-Tucker optimality conditions, to which LIBSVM adds a heuristic to improve the decrease in the objective function. In this paper, and after presenting a simple derivation of the updating procedure based on a greedy maximization of the gain in the objective function, we show how cycle-breaking techniques that accelerate the convergence of support vector machines (SVM) in classification can also be applied under this framework, resulting in significantly improved training times for SVR.     

An accuracy-oriented self-splitting fuzzy classifier with support vector learning in high-order expanded consequent space

This paper proposes a self-splitting fuzzy classifier with support vector learning in expanded high-order consequent space (SFC-SVHC) for classification accuracy improvement. The SFC-SVHC expands the rule-mapped consequent space of a first-order Takagi-Sugeno (TS)-type fuzzy system by including high-order terms to enhance the rule discrimination capability. A novel structure and parameter learning approach is proposed to construct the SFC-SVHC. For structure learning, a variance-based self-splitting clustering (VSSC) algorithm is used to determine distributions of the fuzzy sets in the input space. There are no rules in the SFC-SVHC initially. The VSSC algorithm generates a new cluster by splitting an existing cluster into two according to a predefined cluster-variance criterion. The SFC-SVHC uses trigonometric functions to expand the rule-mapped first-order consequent space to a higher-dimensional space. For parameter optimization in the expanded rule-mapped consequent space, a support vector machine is employed to endow the SFC-SVHC with high generalization ability. Experimental results on several classification benchmark problems show that the SFC-SVHC achieves good classification results with a small number of rules. Comparisons with different classifiers demonstrate the superiority of the SFC-SVHC in classification accuracy.