扩展有限递归进程模型

主要讨论离散事件动态系统的建模问题。由ＫＩｎａｎ和ＰＶａｒａｉｙａ提出的有限递归进程（ＦＲＰ）是描述并发离散事件动态系统的有力工具，但在其ＦＲＰ中，没有定义对数据变量的操作和条件转移算子。本文扩展ＦＲＰ包括这两方面内容。最后举例说明扩展ＦＲＰ的描述能力。     

基于广义最小均方的自适应递归滤波算法

本文提出了一种广义最小均方(GLMS)算法,分析了GLMS算法的性能,并根据GLMS算法,导出一种自适应递归滤波(ARF)算法。在推导中,ARF算法避开了超稳定定理,从而考虑自适应系统的严格正实条件和Popov不等式是不必要的。计算机模拟表明ARF算法具有很高的收敛速度。     

A Standard Model-Theoretic Approach to Operational Semantics of Recursive Programs

In this paper we try to introduce a new approach to operational semantics of recursive programsby using ideas in the“priority method”which is a fundamental tool in Recursion Theory.In lieu ofmodelling partial functions by introducing undefined values in a traditional approach,we shall define apriority derivation tree for every term,and by respecting the rule“attacking the subterm of thehighest priority first”we define transition relations,computation sequences etc.directly based on astandard interpretation which includes no undefined value in its domain.Finally,we prove that ournew approach generates the same operational semantics as the traditional one.It is also pointed outthat we can use our strategy to refute a claim of Loeckx and Sieber that the opperational semanticsof recursive programs cannot be built based on predicate logic.     

基于Wiener结构的软测量模型及辨识算法

Wiener模型结构能有效地表征系统的动态和静态特性, 因此这里首先基于这一结构建立软测量模型, 利用动态与静态子模型分别建立辅助变量与主导变量间的动态与静态关系, 并说明该软测量模型的可行性, 给出模型具体表达式. 其次, 针对所提模型, 提出分步辨识方式获得最优模型参数, 说明其可行性. 再次, 为了减少计算和实现模型在线更新, 这里提出参数辨识递推算法, 并给出软测量模型参数的收敛性结论. 通过实例仿真, 可以看出本文提出模型的可行性, 以及分步辨识方式与递推算法的有效性.     

RECURSIVE GENERALIZED M ESTIMATES FOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Outliers in time series seriously affect conventional parameter estimates. In this paper a robust recursive estimation procedure for the parameters of auto-regressve moving-average models with additive outliers is proposed. Using 'cleaned' residuals from an initial robust fit of an autoregression of high order as input, bounded influence regression is applied recursively. The proposal follows certain ideas of Hannan and Rissanen, who suggested a three-stage procedure for order and parameter estimation in a conventional setting.
A Monte Carlo study is performed to investigate the robustness properties of the proposed class of estimates and to compare them with various other suggestions, including least squares, M estimates, residual autocovariance and truncated residual autocovariance estimates. The results show that the recursive generalized M estimates compare favourably with them. Finally, possible modifications to master even vigourous situations are suggested.     

多步自适应组合预测及其应用

提出一种多步自适应组合预测方法.预测由多个并行不同的多步自适应预测模型组成,最终预测结果,由多个预测模型预测值的线性加权组合决定.本法适用于随机时变动态系统过程的多步预测问题,在某省粮食产量趋势预测中,应用此法获得了良好的效果.     

近似非齐次无偏GM(1,1) 模型的递推解法及应用

针对传统近似非齐次灰建模可能出现参数复数解的问题, 提出无偏灰色GM(1,1) 模型的递推解法, 从而减少由差分方程向微分方程跳跃而导致误差的问题. 给出不同初始条件下非齐次无偏GM(1,1) 模型的递推预测公式,并在此基础上, 将递推公式运用于时间序列分段, 提出基于近似非齐次无偏GM(1,1) 模型的时间序列分段表示方法. 实例结果表明, 所提出的递推模型能够获得较高的拟合精度, 分析结果验证了基于灰色预测模型在时间序列分段表示中的有效性和实用性.

     

基于曲率匹配和递归排序的自适应排样算法

为提高盘状毛坯的使用率,提出一种口腔修复加工中模型边界在盘状毛坯中的排样算法,主要包括边界匹配、多边形定位及递归排序.首先基于协方差矩阵及矩阵SVD分解算法对模型多边形进行分段,并采用等弧长曲线采样曲率匹配确定待匹配模型边界;然后依毛坯边界角度分布及沿圆周排样的思想确定模型轮廓的旋转和平移定位;最后提出一种基于包络率的递归排序算法,对在排样过程中发现的大孔洞可动态地调整排样顺序.实验结果表明,该算法可以处理不规则模型边界在任意形状毛坯中的排样,能有效地降低加工成本.     

Pulse-order recursive method for inverse covariance matrix computation applied to space-time adaptive processing

The conventional space-time adaptive processing(STAP) method such as the typical sample matrix inversion(SMI)-based STAP method is difficult to implement for a practical system because intense computational complexity arises in calculating the inversion of a space-time covariance matrix directly.According to the block Hermitian matrix property of space-time covariance matrix,a new pulse-order recursive method is proposed in this paper to calculate the inverse covariance matrix for the STAP adaptive weight,which can reduce the computational complexity significantly.The proposed method requires initially calculating the inverse covariance matrix of the first pulse-order recursively based on the block Hermitian matrix property.In the following,the inversion of space-time covariance matrix is obtained recursively based on the previous pulse-order inverse covariance matrix.Next,the STAP adaptive weight is calculated based on the inversion space-time covariance matrix previously obtained.Compared with the conventional SMI-based STAP algorithms,the computational complexity of the proposed method is reduced to more than 50% for the same clutter suppression performance.This method can be applied to practical systems benefiting from small computational complexity and stable clutter suppression performance.     

二叉树的先序遍历和中序遍历的非递归算法

从二叉树先序遍历递归算法的执行过程的分析入手,总结出二叉树先序遍历的实质,从而得出利用栈的二叉树的非递归算法。最后,再从分析二叉树中序遍历与先序遍历过程实质的不同之处,得出了二叉树中序遍历的非递归算法。重点在于对二叉树先序和中序遍历过程实质的分析。