基于支持向量机的纺纱质量预测模型研究

纱线的生产是一个多环节的复杂工业过程．其质量控制大多需要依赖领域专家的个人经验,为此提出一种基于支持向量机的纱线质量预测模型．探讨了模型选择与验证问题,并利用“网格搜索”法对模型参数进行了优化,试验结果表明,在小样本和“噪音”数据环境下．支持向量机模型仍能保持一定的预测精度,与人工神经网络模型相比,更适应于真实纺纱生产过程．     

基于人工神经网络的烧结终点预报系统

烧结终点提前预报对于稳定终点,进而提高烧结矿强度和产量、降低能耗有重要意义.为解决烧结过程大滞后环节和烧结终点难以测量的困难,文章介绍了烧结终点的软测量方法;提出了一个新的预报参数——风箱废气温度曲线拐点;将BP神经网络应用于烧结终点的预报,实现了准确地预报烧结终点的状态和生产操作指导.     

Receding horizon control via Bolza-type optimization

This paper deals with the stabilization problem of nonlinear control systems. A variant of the receding horizon control method is proposed which is based on the solution of a certain Bolza problem. The stabilizing property of the method is proved in the case of Lipschitz continuous value function. This version of receding horizon scheme can be considered as a straightforward generalization of the so-called Fake Riccati Equation Technique for nonlinear constrained systems.     

Application of neural networks in forecasting engine systems reliability

This paper presents a comparative study of the predictive performances of neural network time series models for forecasting failures and reliability in engine systems. Traditionally, failure data analysis requires specifications of parametric failure distributions and justifications of certain assumptions, which are at times difficult to validate. On the other hand, the time series modeling technique using neural networks provides a promising alternative. Neural network modeling via feed-forward multilayer perceptron (MLP) suffers from local minima problems and long computation time. The radial basis function (RBF) neural network architecture is found to be a viable alternative due to its shorter training time. Illustrative examples using reliability testing and field data showed that the proposed model results in comparable or better predictive performance than traditional MLP model and the linear benchmark based on Box–Jenkins autoregressive-integrated-moving average (ARIMA) models. The effects of input window size and hidden layer nodes are further investigated. Appropriate design topologies can be determined via sensitivity analysis.     

On the solution of the tracking problem for non-linear systems with MPC

This paper presents a Model Predictive Control (MPC) algorithm for non-linear systems which solves the tracking problem for asymptotically constant references. Closed-loop stability of the equilibrium and asymptotic zero-error regulation are guaranteed. The performance of the method is discussed with the classical Continuous Stirred Tank Reactor (CSTR) control application.     

隐式GPC算法在重介分选过程控制中的研究

为解决重介分选过程中重介质悬浮液密度与液位过程模型失配问题,引入隐式GPC算法对模型参数进行在线辨识,实现对重介质悬浮液的密度与液位解耦控制。仿真结果表明,针对重介分选过程模型具有大滞后、强耦合的这一特性,隐式GPC算法控制效果较好,抗干扰能力强,在模型失配的情况下,仍然保持系统的输出稳定。     

模糊预测控制在时滞系统中的应用

几乎所有控制中都存在时滞,也叫死区时间,在此时间里,生产过程的实际值根本不会对控制器的调节作用起任何反应.时滞的存在会带来超调,震荡等诸多问题.测量方面有了时滞,控制器不能及时发现变量的变化,控制方面有了时滞,控制作用不能及时产生效应.本文结合模糊控制和模糊预测算法,预测控制量和输出量,减少了时滞,提高了控制质量.     

无线体域网MAC层IEEE802.15.6协议研究

在无线体域网中,IEEE 802.15.6协议可以满足不同的体域网应用要求。控制无线信道和能量消耗的MAC层协议在保障高服务质量,低功耗和高数据传输率方面有更好的保障。通过与IEEE 802.15.4协议进行实验对比,表明区分业务优先级的IEEE 802.15.6协议在低能耗的前提下,保证了高优先级业务传输的可靠性,具备更低的数据丢包率,适用于各种要求高数据传输率,高服务质量和高安全性的医疗和非医疗应用。     

Control of dynamic keyhole welding process

Weld joint penetration control is a basic research topic in the welding research community. The authors propose using an innovative plasma arc welding process referred to as the quasi-keyhole process to achieve less application-dependent weld joint penetration sensing and control. To control the quasi-keyhole process, the peak current and keyhole sustaining current are selected as the control variables to maintain the keyhole establishment and sustaining periods at desired values. The dynamic quasi-keyhole process is approximated by a linear model with interval parameters. A control algorithm has been developed for the multivariable interval quasi-keyhole process based on a predictive control algorithm for interval SISO models. Experiments have been conducted to test the effectiveness of the control system developed.     

预测控制在工业锅炉汽包水位中的应用研究

根据工业锅炉汽包水位的动态特点,提出了汽包水位预测控制方案,采用一种基于BP神经网络预报的动态矩阵预测控制新算法,在该算法中,先用BP神经网络辨识对象模型,同时预测对象的未来输出.该方法解决了非线性、时变对象难以建模及控制的问题,仿真结果验证了这一算法的可行性.