Neural Network Based Algorithm for Dynamic System Optimization

A class of artificial neural networks with a two‐layer feedback topology to solve nonlinear discrete dynamic optimization problems is developed. Generalized recurrent neuron models are introduced. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. A comparative analysis of the computational requirements is made. The analysis shows advantages of this approach as compared to the standard dynamic programming algorithm. The technique has been applied to several important optimization problems, such as shortest path and control optimal problems.     

Design and experimental evaluation of a robust force controller for an electro-hydraulic actuator via quantitative feedback theory

This paper presents the design and experimental evaluation of an explicit force controller for a hydraulic actuator in the presence of significant system uncertainties and nonlinearities. The nonlinear version of quantitative feedback theory (QFT) is employed to design a robust time-invariant controller. Two approaches are developed to identify linear time-invariant equivalent model that can precisely represent the nonlinear plant, operating over a wide range. The first approach is based on experimental input–output measurements, obtained directly from the actual system. The second approach is model-based, and utilizes the general nonlinear mathematical model of a hydraulic actuator interacting with an uncertain environment. Given the equivalent models, a controller is then designed to satisfy a priori specified tracking and stability specifications. The controller enjoys the simplicity of fixed-gain controllers while exhibiting robustness. Experimental tests are performed on a hydraulic actuator equipped with a low-cost proportional valve. The results show that the compensated system is not sensitive to the variation of parameters such as environmental stiffness or supply pressure and can equally work well for various set-point forces.     

利用API函数扩展PowerBuilder的功能及其实例

介绍了扩展ＰＢ功能的ＡＰＩ编程方法及其具体步骤,并给出了一个完整例子。     

用Delphi4编写查询本机动态IP地址程序

由于在拨号上网时的 IP地址是由 ISP动态分配的 ,一般用户并不知道它的具体值 ,但在某些特殊的情况下 ,我们需要知道本机确切的 IP地址 ,因此介绍了用 Delphi 4编程 ,通过调用 Windows Sockets函数获取本机动态 IP地址的方法。     

一种复杂非线性系统的智能变结构控制方法

采用一种智能结构方法实现复杂非线性系统的控制,给出了设计原则,并用第四代歼击机飞行控制系统设计一个复杂实例来说明方法的有效性,实例系统中包含了两种控制律：增益调参和非线性动态逆控制律,两种控制律都包括了对发动机推力矢量喷管的控制。文中采用了一种基于状态的控制律切换算法进行两类控制律的切换,考虑了变结构系统切换瞬态问题和强迫振荡问题,并给出了简便的解决方法。     

Cycles in economic systems with open labor markets

A dynamic macromodel of an economic system with a monopsonic labor market and variable labor resources is considered. The initiation conditions for classical business cycles are investigated for this model. Bifurcation analysis of the model is performed to estimate the effect of the minimum wage on the way such cycles form. The results of theoretical analysis are supplemented with numerical experiments. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 48–72, July–August 2008.     

主蒸汽母管压力自动控制及其实施方案

针对主蒸汽母管压力自动控制难以实现的问题,设计了一种独立的母管压力控制系统,规划了它与各台锅炉DCS之间的信息交换方案,提出了调压锅炉在线识别及蒸汽母管总负荷给定的计算方法;应用等微增率准则进行锅炉负荷的优化分配,得到一种能兼顾控制品质和机组经济性的母管压力自动控制方案。应用结果表明,该方案易于实现,且有很高的控制精度和很好的节煤效果。     

带种族遗传算法的神经网络辨识器及其应用

为了解决油田动态系统建立数字模型难和模型训练难的问题,以多层动态前向网络为模型框架,给出了一种新的油田系统神经网络辩识器,它对上述系统具有通用性。为了训练该辩识器和避免极值问题,模拟生物种族进化机制,设计了新的种族遗传算法。该算法是由改进的带共享机制的遗传算法、启发式山峰聚类算法和新的Powell算法有机结合构成的新型混合算法。应用表明,种族遗传算法具有极强的全局和局部搜索能力,这确保了上述神经网络辩识器具有很高的预测精度,平均相对误差在1%以内。     

基于邻居信息的蓝牙分散网动态构建算法

蓝牙网络的低功耗,无线通讯特性使其在无线网络应用领域有着广泛前景。对于组网算法的探讨有利于促进其在实际中的应用。根据设备的邻居结点信息,递归地在同一过程中完成匹克网和分散网的创建。这种组网算法避免了随机组网的盲目性,提高了分散网的稳定性和灵活性,使匹克网的分布尽量均匀,减小匹克网间的相互干扰,从而提高了网络的通讯效率。     

基于PXI总线的多型号战术导弹动态测控系统的研究与开发

阐述了基于PXI总线和LabWindows/CVI软件平台的战术导弹测控系统的研制方案,结合信号特性对系统的组成和总体技术结构进行了分析,介绍了软硬件设计方法及系统容错设计;从导弹控制系统分析、设计和动态性能评估的角度出发,提出了动态测控思想,并有创意地提出了将系统中单一的目标灯替换为目标灯点阵,按导弹运动轨迹,控制目标灯点亮的时序这一思想,最后利用控制指令算法对该思想进行了验证;使用证明,该系统工作可靠,出色地完成了对导弹装备的测试与故障诊断任务。