基于切换机制的多模型自适应混合控制

针对多模型自适应混合控制的性能依赖系统参数估计误差大小的缺点,本文提出了基于切换机制的多模型自适应混合控制.首先对被控系统进行辨识,然后根据参数估计值进行判断.当参数估计值不在最优参数集内时,实行切换策略,重置参数估计值到最优参数集内,用以减小暂态误差,提高暂态性能;当参数估计值在最优参数集内时,实行混合控制,用以平滑过渡过程.文中给出了系统的稳定性和收敛性的证明,最后的仿真实验结果验证了所提出方法的可行性.     

一种仿人智能控制参数整定方法

研究了仿人智能控制器基本形式中的参数确定问题。针对仿人智能控制器基本形式的参数整定缺乏理论指导、过于依赖专家经验和试凑的缺点,力图得到参数取值的解析指导式,解决了仿人智能控制器基本形式的参数确定的难题。分析仿人智能控制器基本形式参数与对象传递函数参数之间的关系,将对象阶跃响应按时间不同分为5个时间段,求取对象的阶跃响应表达式,确定由对象传递函数参数表示的控制器参数的数学表达式。仿真结果表明,该方法具有良好的控制效果。     

动态系统优化与参数估计集成算法的鲁棒性分析

研究动态系统优化与参数估计集成（ＤＩＳＯＰＥ）算法的鲁棒性。定性分析了算法对系统参数和模型参数的鲁棒性,并通过实例对其做了定量化说明。仿真表明,ＤＩＳＯＰＥ算法对系统参数和模型参数具有一定的鲁棒性     

Parameter significance and reductions of soft sets

In this paper, the relationship between any two objects in a soft set is investigated in detail and a novel notion of parameter significance is introduced. Sufficient and necessary conditions on characterizing pseudo parameter reductions and normal parameter reductions with respect to the parameter significance are proposed. Two algorithms for pseudo parameter reductions and normal parameter reductions of a soft set are established. With these algorithms, all pseudo parameter reductions and all normal parameter reductions can be directly evaluated and obtained simultaneously. Several examples show the effectiveness and feasibility of the proposed approach to parameter reductions of a soft set.     

一种新的基于参数估计的故障诊断方法

传统的参数估计方法利用历史数据对参数进行估计,形成一次估计值序列,基于参数估计的故障诊断方法就是利用这个序列对故障进行诊断和分离的,但是,一次估计值序列跟踪真实参数变化存在明显的滞后,本文提出一种边估边修正参数序列的方法,从而克服了参数估计滞后,提高了参数估计的准确度,以此我们为故障检测和分离设计了一种新的补偿最小二乘算法,仿真表明这种算法用于故障诊断是有效的。     

An optimizing design strategy for multiple model adaptiveestimation and control

A method is proposed for designing multiple model adaptive estimators to provide combined state and parameter estimation in the presence of an uncertain parameter vector. It is assumed that the parameter varies over a continuous region and a finite number of constant gain filters are available for the estimation. The estimator elemental filters are shown by minimizing a cost functional representing the average state prediction error autocorrelation, with the average taken as the true parameter ranges over the admissible parameter set. An analogous method is proposed for designing multiple model adaptive regulators to provide stabilizing control in the presence of an uncertain parameter vector by minimizing a cost functional representing the average regulation error autocorrelation, with the average taken as the true parameter ranges over the admissible parameter set. An example is used to demonstrate the improvement in performance over previously accepted design methods     

改进的NURBS曲线预估校正插补方法

现行的NURBS曲线参数预估校正插补方法不能预估最开始的两个参数,而且不能控制预估参数迭代修正的次数。提出改进的NURBS预估校正插补方法,该方法预估插补点参数,根据最大进给步长约束、轮廓误差约束以及法向加速度约束,判定预估参数与实际参数的误差,当误差在允许范围内,则对参数步长增量进行调整,否则进行迭代修正。仿真实验结果表明,新方法通过对参数步长增量的调整,使得预估参数更加接近实际参数,较现有方法有效减少了参数迭代修正的次数,利于实时插补的实现。     

基于核相似性差异最大化的支持向量机参数选择算法

针对支持向量机(SVM)参数选择问题,通过分析SVM近似网络模型及分类原理,提出一种基于核相似性差异最大化的高斯核参数快速选择算法(MSD)。同时,将MSD算法与基于交叉验证的参数搜索算法相结合,构成一种复合SVM参数选择算法(MSD-GS),实现核参数与正则化参数的快速优选。UCI数据的仿真实验表明该算法具有参数选择准确、简便快速、无需数据先验知识等优点,参数选择效果甚至优于遍历式指数网格搜索算法。优选出的参数组合能够使SVM具有较高的泛化性能。     

The normal parameter reduction of soft sets and its algorithm

This paper is concerned with the reduction of soft sets and fuzzy soft sets. Firstly, the problems of suboptimal choice and added parameter set of soft sets are analyzed. Then, we introduce the definition of normal parameter reduction in soft sets to overcome these problems. In addition, a heuristic algorithm of normal parameter reduction is presented. Two new definitions, parameter important degree and decision partition, are proposed for analyzing the algorithm of normal parameter reduction. Furthermore, the normal parameter reduction is also investigated in fuzzy soft sets.     

Set-membership identification of systems with parametric andnonparametric uncertainty

A method for parameter set estimation in which the system model is assumed to contain both parametric and nonparametric uncertainty is presented. In the disturbance-free case, the parameter set estimate is guaranteed to contain the parameter set of the true plant. In the presence of stochastic disturbances, the parameter set estimate obtained from finite data records is shown to have the property that it contains the true-plant parameter set with probability one as the data length tends to infinity     

Missing Output Identification Model Based Recursive Least Squares Algorithm for a Distributed Parameter System

This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least squares algorithm is derived to estimate the unknown parameters of the lumped parameter system. Furthermore, the parameters of the distributed parameter system can be computed by the estimated parameters of the lumped parameter system. The simulation results indicate that the proposed method is effective.     

基于核路径算法的支持向量回归机参数选择

参数选择是支持向量机研究领域的重要问题.针对核参数的选择,提出一种基于二分法的核参数解路径算法.由于解为核参数的非线性光滑函数,该算法随着参数的更新,可以在已有参数得出的解的基础上通过更新公式进行推导计算,从而求得当前参数所对应的解,其目标函数的极值所对应的参数值即为最优参数解.该算法可以快速地求得最优参数.将该方法应用于双酚A生产过程的质量指标软测量建模,仿真结果表明了该算法的可行性和有效性.     

Adaptive control with the stochastic approximation algorithm: Geometry and convergence

New geometric properties possessed by the sequence of parameter estimates are exhibited, which yield valuable insight into the behavior of the stochastic approximation based algorithm as it is used in minimum variance adaptive control. In particular, these geometric properties, together with certain probabilistic arguments, prove that if the system does not have a reduced-order minimum variance controller, then the parameter estimates converge to a random multiple of the true parameter. An explicit expression for the limiting parameter estimate is also available. With strictly positive probability, the limiting parameter estimate is not the true parameter, and in some cases differs from the true parameter with probability one. If the system possesses reduced-order minimum variance controllers, then convergence to a minimum variance controller in a Cesaro sense is shown. The geometry of the limit set is described. Sufficient conditions are also given for some of these results to hold for parameter estimation schemes other than stochastic approximation.     

Sensitivity operator and approximate algorithm for parameter estimation

An infinite dimensional system is considered and the concept is introduced of the sensitivity operator for the system parameter estimate. The selection of the regularization parameter in the optimization criterion for system identification is discussed. Under an appropriate inequality condition on the regularization parameter, the sensitivity operator can be approximated either by Neumann series partial sums or by an iterative algorithm. In addition, the inequality condition on the regularization parameter provides the possibility not only of ensuring the well-posedness of the system parameter estimation but also of performing an analysis of the sensitivity to observation of the system parameter estimate.     

分布参数与集中参数耦合系统的极点配置和镇定向题

在工程控制系统中很重要的情形是受控对象是分布参数系统,由网络组成的装置——控制器是由常微分方程描述的集中参数系统.本文第一部分的讨论表明,通过控制器的设计和观测位置的选择,能使分布参数系统和集中参数系统闭合形成的耦合闭环系统的算子具有预先指定的点谱.本文的第二部分给出了能使耦合闭环系统渐近稳定的必要条件和充分条件.     

Two-step estimators of the scale parameter of the Weibull distribution

An efficient two-step method of estimating the scale parameter of the Weibull distribution is presented and compared to other estimation procedures. The shape parameter is obtained by a procedure other than maximum likelihood and then substituted into the maximum likelihood formula for the scale parameter. Three two-step and four one-step estimators were compared using a Monte Carlo simulation. When the shape parameter is less than one, the two-step estimator using a generalized least-squares estimate of the shape parameter was best in terms of observed relative efficiency. Maximum likelihood was best, but followed closely by the generalized least-squares estimator when the shape parameter is greater than one.     

Analysis of wavelet estimation of self-similar traffic parameters in the Simulink model

The queue model P/M/1/K with the Hurst parameter (self-similarity parameter) estimator in the Simulink environment is described in the present paper using wavelet transformation. The estimation error of the parameter is analyzed depending on the parameter’s value, as well as the number of measurements based on which the parameter is estimated. This is an experimental paper aimed at accumulating knowledge on the practicability of estimating the Hurst parameter and on the estimation accuracy in the case of using wavelet analysis.     

AMT汽车模糊三参数换挡规律研究

为了使AMT汽车自动变速器换挡能够反映路况与车况变化,更好地满足汽车换挡平顺性要求,提出了AMT汽车模糊三参数换挡控制。论文首先给出了模糊三参数换挡控制原理,接着设计了模糊三参数换挡控制器,最后给出了长安羚羊AMT轿车模糊三参数换挡仿真,并与模糊两参数换挡进行了比较。结果表明,采用模糊三参数换挡比采用模糊两参数换挡更加符合驾驶员的换挡经验和习惯,比传统计算法求解换挡规律更简便,易于实现,鲁棒性更强。     

Fixed-parameter tractability of disjunction-free default reasoning

In this paper,the parameter which is the source of the complexity of disjunctionfree default reasoning is determined.It is shown that when the value of this parameter is fixed,the disjunction-free default reasoning can be solved time bounded by a polynomial whose degree does not depend on the parameter.Consequently,disjunction-free default reasoning is fixed parameter tractable.     

A parameter optimization method for radial basis function type models

This paper considers the nonlinear systems modeling problem for control. A structured nonlinear parameter optimization method (SNPOM) adapted to radial basis function (RBF) networks and an RBF network-style coefficients autoregressive model with exogenous variable model parameter estimation is presented. This is an off-line nonlinear model parameter optimization method, depending partly on the Levenberg-Marquardt method for nonlinear parameter optimization and partly on the least-squares method using singular value decomposition for linear parameter estimation. When compared with some other algorithms, the SNPOM accelerates the computational convergence of the parameter optimization search process of RBF-type models. The usefulness of this approach is illustrated by means of several examples.