Extreme learning machine for regression and multiclass classification

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification applications directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. This paper shows that both LS-SVM and PSVM can be simplified further and a unified learning framework of LS-SVM, PSVM, and other regularization algorithms referred to extreme learning machine (ELM) can be built. ELM works for the "generalized" single-hidden-layer feedforward networks (SLFNs), but the hidden layer (or called feature mapping) in ELM need not be tuned. Such SLFNs include but are not limited to SVM, polynomial network, and the conventional feedforward neural networks. This paper shows the following: 1) ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly; 2) from the optimization method point of view, ELM has milder optimization constraints compared to LS-SVM and PSVM; 3) in theory, compared to ELM, LS-SVM and PSVM achieve suboptimal solutions and require higher computational complexity; and 4) in theory, ELM can approximate any target continuous function and classify any disjoint regions. As verified by the simulation results, ELM tends to have better scalability and achieve similar (for regression and binary class cases) or much better (for multiclass cases) generalization performance at much faster learning speed (up to thousands times) than traditional SVM and LS-SVM.     

Dynamics and genetic fuzzy neural network vibration control design of a smart flexible four-bar linkage mechanism

In this paper, a dynamic modeling method and an active vibration control scheme for a smart flexible four-bar linkage mechanism featuring piezoelectric actuators and strain gauge sensors are presented. The dynamics of this smart mechanism is described by the Discrete Time Transfer Matrix Method of Multibody System (MS-DTTMM). Then a nonlinear fuzzy neural network control is employed to suppress the vibration of this smart mechanism. For improving the dynamic performance of the fuzzy neural network, a genetic algorithm based on the MS-DTTMM is designed offline to tune the initial parameters of the fuzzy neural network. The MS-DTTMM avoids the global dynamics equations of the system, which results in the matrices involved are always very small, so the computational efficiency of the dynamic analysis and control system optimization can be greatly improved. Formulations of the method as well as a numerical simulation are given to demonstrate the proposed dynamic method and control scheme.     

Optimal Performance in Tracking Stochastic Signal Under Disturbance Rejection

The analysis method of optimal tracking performance is proposed for multiple‐input multiple‐output (MIMO) linear time‐invariant (LTI) systems under disturbance rejection. An H₂ criterion of the error signal between the output of the plant and the reference signal is used as a measure for the tracking performance. Spectral factorization is applied to obtain the optimal solution of the system tracking error. The explicit expressions are derived for this minimal tracking error with respect to random reference signals under disturbance rejection. It is shown that the nonminimum phase zeros, the zero direction, the unstable poles, the pole direction of a given plant, statistical characteristics of the reference input signal, and disturbance signal have a negative effect on a feedback system's ability to reduce the system error with disturbance rejection. The results show that the optimal tracking performance will further be damaged because of disturbance rejection. Some typical examples are given to illustrate the theoretical results.     

变风量空调系统优化控制策略研究

变风量空调系统是多变量,大滞后、非线性和不确定性的系统,普通的模糊神经网络控制已难以满足其多变量动态控制的要求,为改善变风量空调系统控制性能,本文提出了一种小波模糊神经网络预测控制方法,实现变风量空调的温湿度有效控制.通过小波神经网络预测器在线建立被控对象的数学模型,并用模糊RBF神经网络控制器对所得到的信息在线修正,优化控制器参数,从而改善系统的控制效果.仿真结果表明,小波模糊神经网络预测控制具有很强的鲁棒性和自适应能力,控制精度高,控制效果好,安全可靠等优点,具有广泛的应用价值.     

The International Symposium on Refractories

The International Symposium on Re-fractories jointly organized by theChinese Society of Metals and the ChineseCeramic Society was held in Hangzhou,China, on November 15--18, 1988. The Techni-cal Association on Refractories, Japan,the American Ceramic Society, the Refrac-tories Association of Great Britain andthe German Refractories Association sup-ported the Symposium as internationalcosponsors. The main theme of the Sympo-sium was "Refractory Raw Materials andHigh Performance Refractory Products". Itsmain purpose was to bring together refra-ctories scientists, engineers, manufactur-     

基于内容的静态语义概念视频检索方法研究

本文针对互联网视频检索技术的发展,阐述了目前主流视频搜索引擎的技术现状,分析了互联网视频检索的关键技术,特别是对于视频特征的提取技术。本文的创新点是提出了一种通用的基于内容的静态语义视频检索方法,该方法可以弥补基于文本视频检索的有关不足,并且在TRECVID的视频概念检索数据的静态语义概念中得到验证,运行稳定。     

物联网智能家居系统的研究与设计

智能家居有广阔的应用前景.针对传统家居控制上的缺陷,采用B/S架构设计了智能家居管理系统,采用ZigBee无线网络实现对各类家居设备的信息采集和指令控制.用SQL Server 2005数据库管理系统与家庭网关,进行数据的传递与接收.通过Web浏览器,用户可以实现对智能家居系统的管理.     

基于RBF-PID的水轮发电机组控制系统

研究水轮发电机组稳定性控制优化问题,水轮发电机组是一个非线性、时变的复杂控制系统,很难建立精确模型。采用常规PID控制策略难以较高的控制精度,超调量大。为提高水轮发电机组控制精度,将自学习较强的RBF神经网络与常规PID相结合,提出一种基于RBF-PID组合的水轮发电机组控制算法。采用RBF神经网络对水轮发电机组控制系统的Jacobian矩阵信息进行在线辨识,实现RBF-PID参数在线自整定。仿真结果表明:RBF-PID组合控制器不仅提高控制系统的精度,而且超调量小、抗扰动能力强,能够很好实现水轮发电机组的稳定性优化控制。     

Study on structure and magnetic entropy changes of Ce2-xPrxFe16.5Co0.5 alloys

A series of Ce2-xPrxFe16.5Co0.5 alloys were preparedby arc melting under purified argon atmosphere. The structure and magnetic entropy changes in Ce2-xPrxFe16.5Co0.5 alloys were investigated by means of X-ray diffraction pattern and MPMS XL-7 magnetometer. The experimental results show that the crystal structure of Ce2-xPrxFe16.5Co0.5 alloys keeps in TH2Zn17-type rhombohedral, and the Curie temperature of Ce2-xPrxFe16.5Co0.5 alloys can be shifted to room temperature around by a composition adjustment. The magnetic entropy changes (-ΔSM) in Ce2-xPrxFe16.5Co0.5 alloys are relatively large, and a platform of magnetic entropy changes appearsnear the temperature TC. Ce2-xPrxFe16.5Co0.5 alloys are the potential working media for magnetic refrigeration with their stable chemical properties and especially low price.     

天然沸石与纳滤组合工艺处理铜突发污染的应用

纳滤工艺被广泛应用于分散式饮用水深度处理,能解决饮用水苦咸化,盐碱化或微污染等常规水处理工艺无法规避的用水风险,在长时间运行过程中还能有效应对重金属等突发污染风险。但重金属污染易加重纳滤膜污染,需要增设预处理装置加以缓冲,实现纳滤装置的长久稳定运行。而天然沸石具有良好的筛分性能和吸附性能,能够有效缓冲重金属对纳滤装置的冲击。试验以重金属铜为例,证明水中铜离子会造成纳滤不可逆污染比例增加,不利于系统稳定运行;天然沸石对铜离子有很好的吸附效果,静态吸附试验表明天然沸石对水中铜离子的最大吸附量为3.03 mg/g,通过正交试验合理设计吸附预处理,能够有效为纳滤工艺提供安全进水,保证纳滤进水铜离子浓度低于10 mg/L。实践工艺中,吸附预处理能有效缓解纳滤膜不可逆污染,而且吸附-纳滤组合工艺对铜离子及部分其他重金属离子表现出良好的处理效果。