北运河运河核心区地表水资源状况分析

通过分析运河核心区汇水关系和北关分洪枢纽的历史变迁,收集整理1981-2018年北运河北关闸和通惠河高碑店站水文资料,计算控制流域的降水量、不同时段来水量、河道生态需水量和下游用水量以及可利用来水量,分析表明:运河核心区地表水资源较充沛,来水量较为稳定,可利用水资源保证程度较高.研究结果对于该区域科学取用水、水资源可持续利用以及相关产业的有效实践有参考意义.     

一种基于网络安全风险评估的入侵检测方法

针对传统的入侵检测系统存在报警数量大、误报率高等缺陷,提出了一种基于网络安全风险评估的入侵检测方法,该方法基于入侵检测结果,引入抗体浓度随入侵强度动态变化这一人工免疫理论的最新研究成果进行网络安全风险的计算,然后根据当前网络面临的实时安全风险动态设置报警策略。实验结果表明,该方法能够实时、定量地计算主机和网络所面临的风险,并极大地降低报警数量和误报率。     

矿用区域温度监测系统研制

针对现有的火灾探测器仅能对已发生的火灾进行探测,而在火灾发生前、环境温度较低时,却无法发出报警信号的问题,设计了一种可应用于非煤矿山的基于测温电缆的区域温度监测系统。该系统采用线状温度传感器连续监测探测区域的最高温度,在火灾未发生时即可发出预警信号,从而避免事故发生或引起火灾。试验结果验证了该系统的可行性。     

基于单片机的分段线性插值算法实现

分段线性插值算法是一种技术比较成熟、实现比较简单的低次多项式插值算法,其曲线逼近程度即插值函数与原函数误差基本满足一般的工业控制场合需要。该算法占用系统资源少,在数据采集和数据处理等工业控制领域,往往使用单片机或嵌入式系统等资源受限处理器,通过分段线性插值算法简化原函数,减少计算量,对于提高系统整体性能具有显著的现实意义。     

基于人工神经网络的银行客户流失分析模型

针对目前中国建设银行存在的客户流失问题,利用BP人工神经网络网络稳定、学习能力强的特点,通过输入变量和输出变量之间的相关性分析,建立银行客户流失分析模型,以此获取即将流失的客户,以便银行做出经营决策,挽留有关用户,确保银行效益不受影响。实验证明,此模型能够很好的对银行客户流失进行预测分析。     

OMAPL138和FPGA的合并单元设计方案

提出了一种基于ARM9+DSP双核处理器OMAPL-138和FPGA的合并单元设计方案。给出了该方案的原理框图,以及具体的程序流程图,并在CCS开发环境下完成了软件编程,实现了合并单元按照IEC61850标准规定的格式进行数字量以太网传输。实验结果证明了该设计方案的可行性与正确性,为合并单元的设计提供了一种新的思路。     

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.