一种改进的变步长LMS自适应算法及分析

现有的变步长LMS算法中,大都采用建立步长因子与误差信号的函数关系的方法,以提高算法的收敛速度和跟踪性能,但由于未考虑输入信号对算法性能的影响,使得当输入信号发生变化后,稳态误差明显增大。为此,在现有算法的基础上,引入了输入信号因子,提出了一种改进算法。该算法可根据瞬时误差和输入信号来调整步长因子,使算法不仅能保持较高的收敛速度和跟踪性能,还可在输入信号变化的情况下,保证较小的稳态误差。理论分析及仿真实验表明新算法的性能优于现有算法。     

Synthesizing variable instruction issue interpreters forimplementing functional parallelism on SIMD computers

Functional parallelism can be supported on SIMD machines by interpretation. Under such a scheme, the programs and data of each task are loaded on the processing elements (PEs) and the Control Unit of the machine executes a central control algorithm that causes the concurrent interpretation of the tasks on the PEs. The central control algorithm is, in many respects, analogous to the control store program on microprogrammed machines. Accordingly, the organization of the control algorithm greatly influences the performance of the synthesized MIMD environment. Most central control algorithms are constructed to interpret the execution phase of all instructions during every cycle (iteration). However, it is possible to delay the interpretation of infrequent and costly instructions to improve the overall performance. Interpreters that attempt improved performance by delaying the issue of infrequent instructions are referred to as variable issue control algorithms. This paper examines the construction of optimized variable issue control algorithms. In particular, a mathematical model for the interpretation process is built and two objective functions (instruction throughput and PE utilization) are defined. The problem of deriving variable issue control algorithms for these objective functions has been shown elsewhere to be NP-complete. Therefore, this paper investigates three heuristic algorithms for constructing near optimal variable issue control algorithms. The performance of the algorithms is studied on four different instruction sets and the trends of the schedulers with respect to the instruction sets and the objective functions are analyzed     

Improving practical exact string matching

We present improved variations of the BNDM algorithm for exact string matching. At each alignment our bit-parallel algorithms process a q-gram before testing the state variable. In addition we apply reading a 2-gram in one instruction. Our point of view is practical efficiency of algorithms. Our experiments show that the new variations are faster than earlier algorithms in many cases.     

A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label

In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.     

一种新的基于吉布斯随机场的视频运动对象分割算法

与现有的视频运动对象分割算法不同, 本文提出一种新的基于吉布斯 (Gibbs) 随机场模型的视频运动对象的分割算法, 该算法将运动对象的运动场作为主分割信息, 空间像素值的一致性作为次要分割信息. 该算法首先对运动矢量场进行累加和滤波处理；然后在 Gibbs 运动场模型的势能函数的定义中引入空间相关影响因子, 采用最大后验概率的方法进行分割；最后细化运动对象边缘. 对多个视频序列的测试, 实验结果表明该算法比现有基于光流的分割算法更准确的分割运动对象.     

Initial state iterative learning for final state control in motion systems

In this work, an initial state iterative learning control (ILC) approach is proposed for final state control of motion systems. ILC is applied to learn the desired initial states in the presence of system uncertainties. Four cases are considered where the initial position or speed is a manipulated variable and the final displacement or speed is a controlled variable. Since the control task is specified spatially in states, a state transformation is introduced such that the final state control problems are formulated in the phase plane to facilitate spatial ILC design and analysis. An illustrative example is provided to verify the validity of the proposed ILC algorithms.     

基于独立分量分析的自适应在线算法

独立分量分析(ICA)是近几年兴起的一种高效的信号处理方法,学习步长的优化问题是自适应ICA重要的一方面,基于变步长思想,定义了一种描述信号分离状态的相似性测度,来衡量输出分量之间的相似性程度,并由此提出一种改进的自适应在线算法。根据相似性程度所反映的信号分离状态自适应调节步长,并建立学习步长和相似性测度变化量的非线性关系,克服了传统算法在信道矩阵变化时对步长自适应调整的不足。性能指标分析和仿真实验证明了算法的收敛性和稳态性能。     

Fuzzy smoothing algorithms for variable structure systems

A variable structure system (VSS) is a control system implementing different control laws in different regions of the state space divided by a set of boundary manifolds. The control input switches from one control law to another when the state crosses the boundary manifolds. In general, the control input may not be smooth when switching at these boundary manifolds and may excite high frequency dynamics. This paper proposes two fuzzy rule based algorithms for smoothing the control input. The merits of these fuzzy smoothing control algorithms are illustrated by two examples: a semiactive suspension system based on optimal control and a direct drive robot arm under discrete time sliding mode control. The controller design for these two examples is a blend of traditional control theoretic approaches and fuzzy rule based approaches     

决策系统故障预测的粗糙集方法

基于粗糙集的基本理论和专家系统的相关知识,对决策系统运行故障的在线实时控制问题进行了研究。首先根据决策系统的系统状态“历史”观测值,在对每一个状态变量确定值域赋值后,定义了系统状态数据矩阵、依赖度以及重要性度量参数结构并进行了分析;然后在给出系统状态变量取舍的判定定理的基础上,利用专家调研加权算法得到的状态变量重要性度量约束容许值,简化决策系统的数据库,导出决策系统故障的控制规则;接着,利用这些判定规则和在线实时观测数据,设计了决策系统故障预测的流程图;最后根据流程图提出了决策系统故障在线实时控制的运行机理。理论分析结果表明,这种设计方案是有效的。     

基于全局优化策略的场景分类算法

提出一种基于全局优化策略的场景分类算法.该算法基于整幅图像提取全局场景特征——空间包络特征.从图像块中提取视觉单词,且定义隐变量表示该视觉单词语义,然后引入隐状态结构图描述整幅图像的视觉单词上下文;在场景分类策略上,构造由相容函数组成的目标函数,其中相容函数度量全局场景特征、隐变量与场景类别标记的相容度,通过求解目标函数的全局最优解推断图像的场景类别标记.在标准场景图像库上的对比实验表明该算法优于当前有代表性的场景分类算法.     

Maximizing power saving with state transition overhead for multiple mobile subscriber stations in WiMAX

In the IEEE 802.16e/m standard, three power saving classes (PSCs) are defined to save the energy of a mobile sub-scriber station (MSS). However, how to set the parameters of PSCs to maximize the power saving and guarantee the quality of service is not specified in the standard. Thus, many algorithms were proposed to set the PSCs in IEEE 802.16 networks. However, most of the proposed algorithms consider only the power saving for a single MSS. In the algorithms designed for multiple MSSs, the sleep state, which is set for activation of state transition overhead power, is not considered. The PSC setting for real-time connections in multiple MSSs with consideration of the state transition overhead is studied. The problem is non-deterministic polynomial time hard (NP-hard), and a suboptimal algorithm for the problem is proposed. Simulation results demonstrate that the energy saving of the proposed algorithm is higher than that of state-of-the-art algorithms and approaches the optimum limit.     

A variable step-size selective partial update LMS algorithm

Selective partial update of the adaptive filter coefficients has been a popular method for reducing the computational complexity of least mean-square (LMS)-type adaptive algorithms. These algorithms use a fixed step-size that forces a performance compromise between fast convergence speed and small steady state misadjustment. This paper proposes a variable step-size (VSS) selective partial update LMS algorithm, where the VSS is an approximation of an optimal derived one. The VSS equations are controlled by only one parameter, and do not require any a priori information about the statistics of the system environment. Mean-square performance analysis will be provided for independent and identically distributed (i.i.d.) input signals, and an expression for the algorithm steady state excess mean-square error (MSE) will be presented. Simulation experiments are conducted to compare the proposed algorithm with existing full-update VSS LMS algorithms, which indicate that the proposed algorithm performs as well as these algorithms while requiring less computational complexity.     

变精度上近似与程度下近似粗糙集模型的正域及其算法

针对变精度近似与程度近似的结合问题及正域的核心地位,组建了变精度上近似与程度下近似粗糙集模型,并定义了其中的正域概念。研究了模型正域与精度量化指标和程度量化指标关联的内涵及意义,得到了模型正域的精确刻画与性质。为了计算模型正域,提出了自然算法与原子算法,并进行了算法分析与算法比较,得到了自然算法与原子算法具有相同的时间复杂性,而原子算法却具有更优的空间复杂性的结论。最后用一个医疗实例对模型正域及其算法进行了分析与说明。变精度上近似与程度下近似粗糙集模型的正域,从膨胀的优势方向完全扩展了经典粗糙集模型的正域,对与精度参数和程度参数相关的必然性知识发现具有意义。     

State inverse and decorrelated state stochastic approximation

Stochastic approximation algorithms for parameter identification are derived by a sequential optimization and weighted averaging procedure with an instructive geometric interpretation. Known algorithms including standard least squares and suboptimal versions requiring less computational effort are thereby derived. More significantly, novel schemes emerge from the theory which, in the cases studied to date and reported here, converge much more rapidly than their nearest rivals amongst the class of known simple schemes. The novel algorithms are distinguished from the known ones by either a different step size selection, and/or by working with a transformed state variable with components relatively less correlated, and/or by replacing the state vector in a crucial part of the calculations by its componentwise pseudoinverse.The convergence rate of the novel schemes in our simulations is significantly closer to that of the more sophisticated optimal least square recursions than other stochastic approximations schemes in the literature. For the case of extended least squares and recursive maximum likelihood schemes, the novel stochastic recursion performs, in loose terms within a factor of 10 (rms error), of the more sophisticated schemes in the literature. An asymptotic convergence analysis for the algorithms is a minor extension of known theory.     

A survey on self-stabilizing algorithms for independence,domination, coloring,and matching in graphs

Dijkstra defined a distributed system to be self-stabilizing if, regardless of the initial state, the system is guaranteed to reach a legitimate (correct) state in a finite time. Even though the concept of self-stabilization received little attention when it was introduced, it has become one of the most popular fault tolerance approaches. On the other hand, graph algorithms form the basis of many network protocols. They are used in routing, clustering, multicasting and many other tasks. The objective of this paper is to survey the self-stabilizing algorithms for dominating and independent set problems, colorings, and matchings. These graph theoretic problems are well studied in the context of self-stabilization and a large number of algorithms have been proposed for them.     

Minimax optimization of stationary states in systems with distributed parameters

The minimization problem for deviations of the stationary state of a system with distributed parameters from a desired state estimated in the uniform metric is considered. A constructive method for its solution using a successive parameterization of controls and the alternance properties of optimal spatial distributions of the controlled variable is proposed. Examples of the use of the proposed computational scheme of the organization of computational algorithms in optimization problems of stationary thermal conductivity processes are discussed.     

基于重量分析的OBDD变量排序算法

有序的二叉判决图（ＯＢＤＤ）是布尔表达式的一种有效表示方法，但它的体积对变量排序具有较强的依赖性。本文提出一种电路结构图，并在此基础上定义了原始输入重量和节点重量等参数，并建立了用重量分析来指导的ＯＢＤＤ变量排序算法。由于从考虑变量对输出函数的影响出发与从考虑ＯＢＤＤ节点共享性出发对变量排序的要求不同，本文分别设计了两类算法。实验结果表明，本文对大多数标准电路变量排序的效果都优于国际上的同类算法，     

基于模型检测的数据流异常测试技术研究

程序的执行体现为数据在变量中的流动。对C/C++源代码中变量定义使用情况进行分析,针对变量未赋值就使用、变量重复赋值和变量定义后未使用三种数据流异常情况,使用程序阅读自动机,把程序转换为变量状态机,使用ALCCTL时序逻辑和模型检验工具,验证程序是否满足定义的可信模式。提出了新的静态查找变量使用故障的方法。该方法已应用于面向故障的软件测试系统中。     

Adaptive combination of affine projection and NLMS algorithms based on variable step-sizes

Considering the filters with variable step-sizes outperform their fixed step-sizes versions and the combination algorithms with proper mixing parameters outperform their components, a combination algorithm consisting of improved variable step-size affine projection (I-VSSAP) and normalized least mean square (I-VSSNLMS) algorithms, of which the former is fast and the latter is slow, is proposed for stationary environment. Different from the combination algorithms whose components are updated independently, the variable step-sizes components are adapted using the same input and error signals, and their step-sizes are derived via the mean-square deviation (MSD) of the overall filter. Therefore, the components reflect the working state of the combination filter more accurately than their fixed step-sizes versions. The mixing parameter is obtained by minimizing the MSD and gradually decreases from 1 to 0. Therefore the proposed algorithm has a performance similar to I-VSSAP and I-VSSNLMS in the initial stage and steady-state respectively. Simulations confirm that the proposed algorithm outperforms its components and its fixed step-sizes version. The mixing parameter is artificially set to 0 when the difference between the MSDs of two adjacent iterations is below a user-defined threshold, then the proposed algorithm degrades to I-VSSNLMS and exhibits a less computational complexity than AP algorithm.