Complexity Measures for Process Evolution

The foundation for process evolution research is the modelling of process structural complexity. With such a model, process systems can be directed toward acquiring good maintainability attributes according to the principles of engineering. In this paper a process management (PM) model is developed to acquire directly from the target process codes the knowledge hidden among and within components of process systems. With the knowledge acquired by the PM model, process structural complexity measures in terms of partitioning, restructuring and rewriting criteria are developed; a systematic process remodularization schema is derived, and algorithms for scheduling process changes are presented. These criteria and mechanisms are used to guide the process production chain.     

预测控制在绝干浆量控制中的应用研究

在造纸生产中把纸浆浓度和流量的乘积作为绝干浆量变量，控制策略将绝干浆量指标纳入控制器的设计，对浆流量和浓度采用综合控制，解决了造纸生产中难以控制的定量问题。应用结果表明，预测先进控制算法在解耦、克重指标、跟踪平稳性等方面表现出了良好的性能，表明了造纸对象上的成功应用。     

面向数据包丢失补偿的网络化广义预测控制

针对网络化控制系统中存在的数据包丢失,考虑了基于状态空间模型的网络化广义预测控制问题;在假设反馈通道和控制通道的数据包丢失过程确定可知的情况下,提出了一种采用预测器和预测控制器分别补偿反馈通道和控制通道的数据包丢失对系统性能影响的方法,通过把广义预测控制问题转化为滚动线性二次型最优跟踪问题,基于动态规划给出了网络化广义预测控制器的设计方法,并基于Ricoati差分方程非负定解的单调性,给出了末端加权矩阵保证系统稳定性的充分条件,最后通过仿真验证了所提出方法的有效性.     

由符号函数与绝对值函数派生的非线性函数

人工智能控制中逻辑判断必不可少。因为可以采用简单的非线性函数进行逻辑判断,所以在实际应用中,可以用适当构造的非线性函数来实现部分的智能控制。因此,提出了一种采用符号函数和绝对值函数构造特殊非线性函数的方法,并讨论了利用这些特殊的非线性函数逼近任意的第一类非连续函数的问题。     

On the Complexity of Function Learning

The majority of results in computational learning theory are concerned with concept learning, i.e. with the special case of function learning for classes of functions with range {0, 1}. Much less is known about the theory of learning functions with a larger range such as or . In particular relatively few results exist about the general structure of common models for function learning, and there are only very few nontrivial function classes for which positive learning results have been exhibited in any of these models.We introduce in this paper the notion of a binary branching adversary tree for function learning, which allows us to give a somewhat surprising equivalent characterization of the optimal learning cost for learning a class of real-valued functions (in terms of a max-min definition which does not involve any learning model).Another general structural result of this paper relates the cost for learning a union of function classes to the learning costs for the individual function classes.Furthermore, we exhibit an efficient learning algorithm for learning convex piecewise linear functions from ^d into . Previously, the class of linear functions from ^d into was the only class of functions with multidimensional domain that was known to be learnable within the rigorous framework of a formal model for online learning.Finally we give a sufficient condition for an arbitrary class of functions from into that allows us to learn the class of all functions that can be written as the pointwise maximum ofk functions from . This allows us to exhibit a number of further nontrivial classes of functions from into for which there exist efficient learning algorithms.     

A Comparison of Measures of Control Flow Complexity

In attempting to describe the quality of computer software, one of the more frequently mentioned measurable attributes is complexity of the flow of control. During the past several years, there have been many attempts to quantify this aspect of computer programs, approaching the problem from such diverse points of view as graph theory and software science. Most notable measures in these areas are McCabe's cyclomatic complexity and Halstead's software effort. More recently, Woodward et al. proposed a complexity measure based on the number of crossings, or "knots," of arcs in a linearization of the flowgraph.     

极点配置预测函数控制

本文通过在预测函数优化性能指标中引入加权多项式来进行极点配置预测函数控制的研究。通过适当选择加权多项式 ,可将极点配置在给定位置 ,获得所期望的闭环响应特性。同时基于模型脉冲传递函数 ,得到控制量的显式表达式。     

共享控制系统的丢包预测补偿控制算法

对共享控制系统中数据包在因特网传输过程发生丢包的现象进行建模,分析丢包对系统控制性能的影响,利用预测补偿控制原理设计一种基于混合型pi—sigmod模糊神经网络的丢包补偿算法。仿真实验结果表明,该算法能克服丢包对控制系统的性能和稳定性造成的不利影响。     

基于动态损失函数的远程监督关系抽取

关系抽取是信息抽取的主要任务之一,远程监督作为关系抽取中的一种有效的方法,已成功地应用于包含上千关系的大型语料库.然而,远程监督造成的错误标注会影响关系抽取的性能.为了缓解这一问题,现有的远程监督关系抽取方法选择每个实体对中一个最好的句子或通过注意力机制赋予每个句子不同的权重.但这些方法并不能完全解决错误标注的问题.本...     

Learning-Based Complexity Evaluation of Radial Basis Function Networks

Recently, the complexity control of dynamic neural models has gained significant attention. The performance of such a process depends highly on the applied definition of model complexity. On the other hand, the learning theory creates a framework to assess the learning properties of models. These properties include the required size of the training samples as well as the statistical confidence over the model. In this Letter, we apply the learning properties of two families of Radial Basis Function Networks (RBFN's) to introduce new complexity measures that reflect the learning properties of such neural model. Then, based on these complexity terms we define cost functions, which provide a balance between the training and testing performances of the model.     

A Note on Complexity Measures for Inductive Classes in Constructive Type Theory

It is notoriously hard to express computational complexity properties of programs in programming logics based on a semantics which respects extensional function equality. That is a serious impediment to applications of programming logics requiring reasoning about complexity. This paper shows how to use existing mechanisms to define internal computational complexity measures in logics that support inductively defined types, dependent products, and functions. The method exploits a feature of inductive definitions in constructive type theory, namely that implicit proof codes are kept with the objects showing how they are presented in the inductive class. The idea is illustrated by giving a formal inductive definition ofPTimebased on ideas from Leivant's work and on Bellantoni and Cook's approach. Then a complexity measure is defined on elements of this class. This paper discusses the limitations of this idea and the need forfaithfulnessguarantees that link internal complexity classes to the implementation of the logic. The paper concludes with a definition ofresource bounded logicsand a discussion of interesting lines of investigation of these logics which have the potential to make practical uses of results from computational complexity theory in formal reasoning about the efficiency of programs.     

一种具有PI结构的时滞系统APFC控制

提出了一种具有PI结构的时滞系统自适应预测函数控制（APFC）,该算法通过在性能指标函数中引入预测输出误差和预测输出增量误差,使最终形成的控制器具有比例-积分（PI）结构。结合了预测控制具有较好的处理时滞、快速跟踪和PID调节器抗干扰性强、鲁棒性强的优点,并且模型参数是通过带遗忘因子的递推最小二乘算法在线辨识得到。在具有时滞的工业过程数学模型上进行仿真研究,结果表明,该算法具有较好的跟踪效果、抗干扰能力和鲁棒性。     

Extremal Relations between Additive Loss Functions and the Kolmogorov Complexity

Conditions are presented under which the maximum of the Kolmogorov complexity (algorithmic entropy) K(₁... _N ) is attained, given the cost f( _i ) of a message ₁... _N . Various extremal relations between the message cost and the Kolmogorov complexity are also considered; in particular, the minimization problem for the function f( _i ) – K(₁... _N ) is studied. Here, is a parameter, called the temperature by analogy with thermodynamics. We also study domains of small variation of this function.     

基于小波基函数的预测函数控制

针对传统PFC只能对预测时域内有限的几个拟合点进行拟合,未考虑整个预测时域整体逼近误差性能的优化,系统的控制品质受到损害的问题,提出一种基于小波基函数的预测函数控制方法.利用小波的紧支局部性和多尺度分析特性,对基函数的个数和位置分布可根据系统控制精度和不同预测时段的不同逼近要求进行灵活设置,既保证了整体误差性能的优化,又突出了重要拟合点的逼近要求,并实现了优化变量的集结.理论分析和仿真应用表明,该方法比传统的PFC有更好的快速性和抗干扰性,对于快速系统应用场合具有良好的应用前景.     

Suboptimal feedback control for nonlinear systems

A method is presented for synthesizing suboptimal feedback control laws for nonlinear systems optimized with respect to a quadratic performance index. The proposed method allows the designer to easily calculate a second-order approximation to the optimal control.     

产品重构设计中基于最小损失函数的零件选择算法

选择和重用已有零件设计方案是复杂产品创新的基础，零件选择的结果需要满足特定的设计约束。文中讨论了在零件选择过程中约束的不同类型。结合可选择域的概念，通过最小损失函数的方法，给出了同时满足零件个体约束和零件重构约束的启发式零件选择算法。该算法利用最小损失函数对可选择域大小进行预测，优化了搜索过程，并通过一个应用实例，对运用该算法前后的情况进行对比，得出了相关结论。     

起重机电机定子异常失电故障保护功能检测系统设计及应用

针对我国起重机电机定子异常失电故障隐患问题,传统人工检测方法效率低且在检测过程中容易遗漏隐患的缺陷,严重威胁到日常生产作业安全.根据起重机安全检规相关规定和检测方法,设计出一款起重机电机定子异常失电故障保护功能检测系统,使用人机交互触摸屏界面可以实现检测系统功能自检复位,所提出分类异常故障模拟实验检测方法自动化程度高,...     

Using Rule Quality Measures for Rule Base Refinement in Knowledge-Based Predictive Maintenance Systems

Abstract

As today’s manufacturing domain is becoming more and more knowledge-intensive, knowledge-based systems (KBS) are widely applied in the predictive maintenance domain to detect and predict anomalies in machines and machine components. Within a KBS, decision rules are a comprehensive and interpretable tool for classification and knowledge discovery from data. However, when the decision rules incorporated in a KBS are extracted from heterogeneous sources, they may suffer from several rule quality issues, which weakens the performance of a KBS. To address this issue, in this paper, we propose a rule base refinement approach with considering rule quality measures. The proposed approach is based on a rule integration method for integrating the expert rules and the rules obtained from data mining. Within the integration process, rule accuracy, coverage, redundancy, conflict, and subsumption are the quality measures that we use to refine the rule base. A case study on a real-world data set shows the approach in detail.