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.     

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。     

EFFECT OF RARE EARTH ADDITIONS ON THE IMPACT FRACTURE OF Cr18Nil8Si2 STEEL

<正> Cr 18 Ni 18 Si 2钢经热处理后的低温冲击断口,其中加稀土的为穿晶断口,而未加稀土的为混合型断口。 试验用钢用高频感应炉冶炼,锭重20kg,锻成15×15mm方棒,再加工成冲击试样。其化学成分如下。稀土的加入是将一号混合稀土合金缚在钢棒上插入熔池。     

Adsorption characteristics of hexadecyl ammonium with different numbers of carbon chains in montmorillonite and the structure of the prepared composites

Journal of Porous Materials - The dynamics and thermodynamics of adsorption of hexadecyl ammonium with different numbers of carbon chains in montmorillonite (Mt) with different layer charge density...     

钢丝绳张紧装置在大行程拖链上的应用

介绍了给DY3000×20m数控机床大行程拖链增设的张紧装置,该装置解决了拖链无法自动复位的问题,使用方便、经济可行。     

固态锂电池研究及产业化进展

相比于传统液态锂离子电池,固态锂电池(SSLB)用固态电解质代替有机电解液,安全性和能量密度均大大提升,可以有效降低电动汽车安全隐患和缓解用户续航里程焦虑.固态电解质作为电子绝缘体和离子导体是SSLB核心要素之一,同时其存在离子电导率低、界面阻抗大和界面稳定性差等问题.通过研究近期相关文献,对硫化物固态电解质、氧化物固态电解质、聚合物固态电解质以及复合固态电解质锂电池的离子导电机理、研究进展、存在的主要问题及解决方案进行了综述和讨论.对于提高离子电导率,重点介绍了调整固态电解质组分的方法.对于改善界面问题,主要介绍了界面设计和制成工艺方法改善思路.综合分析表明,通过掺杂和包覆改性固态电解质、探索先进界面研究和诊断技术并指导设计具有优良锂离子传输能力的界面、创新和优化工艺能有效地提升固态电解质综合性能.最后列举了国内外重点企业的固态锂电池产业化进程,对固态锂电池未来应用前景进行了分析和展望.     

冲击荷载作用下热处理花岗岩动态力学特性研究

为研究冲击速度和热处理温度对黑云母花岗岩动态力学特性的影响,利用改进的霍普金森压杆系统对25~800 ℃共9个温度等级的热处理试样分别进行3种弹速下的冲击压缩试验。试验结果表明：随着冲击速度的增加,25~700 ℃热处理试样的应力—应变曲线由“Ⅱ型”转变为“Ⅰ型”,而800 ℃热处理试样均表现出“Ⅰ型”应力—应变曲线特征。同一温度热处理下试块的峰值应力、应变和平均应变均随动荷载的升高而增大。相同的冲击速度下,300 ℃热处理试样的动力学性能有所改善,500 ℃后试样的动力学性能开始逐渐劣化。同一温度热处理试样的破碎程度随冲击速度的增加而增加;相同冲击速度下,热处理试样的破碎程度随温度的升高先减弱后增强。