复杂系统的递阶模糊辨识

针对Takagi_Sugeno模糊模型 (T_S模型 )严重的维数灾问题, 借鉴GMDH算法, 提出了一种新的复杂系统递阶模糊辨识方法. 本文首先详细描述了由两输入变量的特殊T_S模型所组成的递阶模糊模型 ;然后提出了具体的辨识该递阶模糊模型的方法. 该方法的特点是 :a)在结构辨识阶段, 用FCM模糊聚类方法评价系统中每个输入变量的重要性, 以便构造合理的递阶模糊模型 ;b)预先合理地确定了所要辨识的参数的初始值, 用扩展卡尔曼滤波方法可很快地得到这些参数. 最后, 给出的仿真实例说明了本文辨识方法的有     

矩阵伪逆的递归计算和程序

在系统辨识中,常常需要计算矩阵的伪逆。本文叙述了递归计算n×m(n相似文献   

用PAR方法构建三种Java集合类

分析了抽象程序设计语言Apla(Abstract Programming Language)定义的集合摄作的实现算法思想,用PAR方法（分划递推法,Partition-and-Recur)推导了实现集合操作的Apla程序,并分别将这些抽象程序用三种数据结构（向量（Vector)、数组（Aarray)、链表（noldep,自定义））实现,构成了自定义Java集合类库。该类库可用于Apla-Java程序自动转换系统和体现数据抽象思想的Java程序开发。     

非线性离散系统的贝叶斯-高斯神经网络逆模型设计

针对非线性系统逆模型的学习问题,提出一种基于贝叶斯-高斯神经网络(BGNN)的设计方法.BGNN模型的训练分为两个步骤,首先利用群智能优化算法进行BGNN的离线结构训练:然后用训练好的BGNN模型在线整合历史数据,进行非线性系统逆模型的获取.对水轮发电机组非线性系统进行了BGNN逆模型的仿真,结果表明了BGNN逆模型设计方法具有结构简单、在线辨识效果好等优点,适于非线性离散系统的逆模型设计.     

蚁群聚类算法的T-S模糊模型辨识

基于T-S模型,提出一种非线性系统的模型辨识方法。利用蚁群聚类算法来进行结构辨识,确定系统的模糊空间和模糊规则数。在聚类的基础上,利用遗传算法辨识模糊模型的后件加权参数,得到一个精确的模糊模型,从而实现参数辨识。仿真结果验证了该方法的有效性,表明该方法能够实现非线性系统的辨识,辨识精度高,可当作复杂系统建模的一种有效手段。     

仿真中的并行性初探

本文以仿真语言为背景,探讨了连续系统和离散系统仿真过程中的并行性,给出开发并行性的若干途径,并讨论了其中的一些方法及有待解决的问题。特别是给出了离散系统中若干模型的并行处理方法,这些方法可推广到一般的离散系统模型。     

利用功率谱分解与系统辨识法对离散系统模型进行降阶处理

本文介绍一种离散系统模型降阶的混合方法.从能量影响的观点到系统的输出,系统中 处于主导地位的特征值会在系统输出产生优势的动态模态,从而确定降阶模型传函中分母的 参数估值,再用辨识技术求出分子参数.降阶模型与原系统相比,暂态响应与稳态响应中均有 很好的逼近.     

基于模糊神经网络的非线性系统模型的辨识

该文提出一种非线性系统的模型辨识方法．利用关系聚类法来进行结构辨识，从而自动获得模糊规则库，并可以得到模糊系统的初始参数，在聚类的基础上，构造一个与之相匹配的模糊神经网络，用它的学习算法来训练网络，得到一个精确的模糊模型，从而实现参数辨识，通过对两个非线性系统辨识的仿真结果验证了该方法的有效性。     

面向对象语言和C++讲座 第五讲 C++语言的基本类库

一、引言 面向对象的程序设计语言所带来的最主要的好处就是:以这种语言设计结构合理的系统能够达到高度的复用。 软件复用可以有多种形式,我们可以复用几行代码构成的代码段、特定的类或按一定逻辑关系联系起来的类。复用代码段是最简单的复用形式,但这种复用形式带来的好处是微乎其微的,因为代码必须在多个程序之间重复。面向对象的程序设计语言利用现有的类并通过继承将它们特定化、参数化,就可以复用一组有逻辑关系的类,从而达到更大程度的复用。这种以一定的逻辑关系结合起来的类的集合就是类库。     

一种新的模糊聚类神经网络及其在语音信号系统辨识中的应用

主要解决语音信号模型的系统辨识问题.针对过去的模糊聚类算法进行系统辨识时逼近性能不理想的问题,提出了一种新的模糊聚类神经网络(FCNN).该方法以模糊系统模型为基础,将每个状态看作一个模糊系统,用连续的若干序列作为系统的输入,利用改进的模糊聚类辨识算法构成一种新型的模糊聚类神经网络,对系统的输出进行预测.通过语音信号系统辨识的实验,验证了本网络的有效性.     

SYSTEM THEORETIC FORMALISMS FOR COMBINED DISCRETE-CONTINUOUS SYSTEM SIMULATION

In this paper we present an approach to combined discrete-continuous modelling which can be used to model and simulate an intelligent multi-layer control architecture as can be found in high autonomy systems. The modelling approach is based on system theoretical concepts; the three system specification formalisms-differential equation, discrete time, and the discrete event system specification formalism-have been combined to facilitate multi-formalisms modelling. Simulation concepts are based on the abstract simulator concept for discrete event simulation developed by Zeigler. Similar simulation methods have been developed to simulate modular, hierarchical discrete time and differential equation specified systems as well as multi-formalism models. Included in the paper are several examples of multi-formalism models together with the simulation results from the STIMS environment-an implementation of the modelling and simulation concepts in Interlisp-D/LOOPS.     

Towards scalable synthesis of stochastic control systems

Formal synthesis approaches over stochastic systems have received significant attention in the past few years, in view of their ability to provide provably correct controllers for complex logical specifications in an automated fashion. Examples of complex specifications include properties expressed as formulae in linear temporal logic (LTL) or as automata on infinite strings. A general methodology to synthesize controllers for such properties resorts to symbolic models of the given stochastic systems. Symbolic models are finite abstractions of the given concrete systems with the property that a controller designed on the abstraction can be refined (or implemented) into a controller on the original system. Although the recent development of techniques for the construction of symbolic models has been quite encouraging, the general goal of formal synthesis over stochastic control systems is by no means solved. A fundamental issue with the existing techniques is the known “curse of dimensionality,” which is due to the need to discretize state and input sets. Such discretization generally results in an exponential complexity over the number of state and input variables in the concrete system. In this work we propose a novel abstraction technique for incrementally stable stochastic control systems, which does not require state-space discretization but only input set discretization, and that can be potentially more efficient (and thus scalable) than existing approaches. We elucidate the effectiveness of the proposed approach by synthesizing a schedule for the coordination of two traffic lights under some safety and fairness requirements for a road traffic model. Further we argue that this 5-dimensional linear stochastic control system cannot be studied with existing approaches based on state-space discretization due to the very large number of generated discrete states.     

CSCW系统体系结构中协作机制的研究

首先对现有的CSCW系统体系中的协作机制进行了认真的分析,进而提出了一种通用的模型运行控制机制－MTI协作模型和相应的协作策略,它解决了以往协作模型对各种协作方式及动态协同支持不足的问题,从而极大地提高了CSCW系统中分布式协作的效率和质量。本文最后对MTI协作模型的协作策略进行了详细的形式化描述。协作模型MTI已成功地应用于国家九五科技攻关项目“面向协作的组件化的系统”的实现中,实践表明该模型有效地提高了系统的性能和协作效率。     

A formal framework for modeling and validating Simulink diagrams

Simulink has been widely used in industry to model and simulate embedded systems. With the increasing usage of embedded systems in real-time safety-critical situations, Simulink becomes deficient to analyze (timing) requirements with high-level assurance. In this article, we apply Timed Interval Calculus (TIC), a real-time specification language, to complement Simulink with TIC formal verification capability. We elaborately construct TIC library functions to model Simulink library blocks which are used to compose Simulink diagrams. Next, Simulink diagrams are automatically transformed into TIC models which preserve functional and timing aspects. Important requirements such as timing bounded liveness can be precisely specified in TIC for whole diagrams or some components. Lastly, validation of TIC models can be rigorously conducted with a high degree of automation using a generic theorem prover. Our framework can enlarge the design space by representing environment properties to open systems, and handle complex diagrams as the analysis of continuous and discrete behavior is supported.     

Stability analysis and controller synthesis for discrete uncertain singular fuzzy systems with distinct difference matrices in the rules

This paper mainly studies an extended discrete singular fuzzy model incorporating the multiple difference matrices in the rules and discusses its stability and design issues. By embracing additional algebraic constraint, traditional discrete Takagi-Sugeno (T-S) fuzzy model can be extended to a generalised discrete singular Takagi-Sugeno (GDST-S) model with individual difference matrices E_i in the locally singular models, where it can describe a larger class of physical or non-linear systems. Based on the linear matrix inequality (LMI) approach, we focus on deriving some explicit stability and design criteria expressed by the LMIs for the regarded system. Thus, the stability verification and controller synthesis can be performed by the current LMI tools. Finally, some illustrative examples are given to illustrate the effectiveness and validity of the proposed approach.     

Extending the DEVS formalism for massively parallel simulation

The use of multiprocessors for discrete event simulation is an active research area where work has focused on strategies for model execution with little regard for the underlying formalism in which models may be expressed. However, a formalism-based approach offers several advantages including the ability to migrate models from sequential to parallel platforms and the ability to calibrate simulation architectures to model structural properties. In this article, we extend the DEVS (discrete event system specification) formalism, originally developed for sequential simulation, to accommodate the full potential of parallel processing. The extension facilitates exploitation of both internal and external event parallelism manifested in hierarchical, modular DEVS models. After developing a mapping of the extended formalism to parallel architectures, we describe an implementation of the approach on a massively parallel architecture, the Connection Machine. Execution results are discussed for a class of models exhibiting high external and internal event parallelism, the so-called broadcast models. These verify the tenets of the underlying theory and demonstrate that significant reduction in execution time is possible compared to the same model executed in serial simulation.     

Fairness of Transitions in Diagnosability of Discrete Event Systems

Failure diagnosability has been widely studied for discrete event system (DES) models because of modeling simplicity and computational efficiency due to abstraction. In the literature it is often held that for diagnosability, such models can be used not only for systems that fall naturally in the class of DES but also for the ones traditionally treated as continuous variable dynamic systems. A class of algorithms for failure diagnosability of DES models has been successfully developed for systems where fairness is not a part of the model. These algorithms are based on detecting cycles in the normal and the failure model that look identical. However, there exist systems with all transitions fair where the diagnosability condition that hinges upon this feature renders many failures non-diagnosable although they may actually be diagnosable by transitions out of a cycle. Hence, the diagnosability conditions based on cycle detection need to be modified to hold for many real-world systems where all transitions are fair. In this work, however, it is shown by means of an example that a system may have some transitions fair and some unfair. A new failure diagnosability mechanism is proposed for DES models with both fair and unfair transitions. Time complexity for deciding diagnosability of DES models with fair and unfair transitions is analyzed and compared with the time complexities of other DES diagnosability analysis methods reported in the literature.     

自动产生一个图书馆的快速原型

AutoPA1.0是一套基于形式化方法的应用程序.它是一个基于需求规范而自动生成软件快速原型的Java代码的软件, 有着坚实、可靠的理论基础.给出一个图书馆管理系统的例子, 阐述如何用UML建立需求模型,然后用AutoPA1.0生成该需求模型对应的软件快速原型的Java代码. 采用的需求模型主要包括一个用况模型和一个概念类模型, 分别用UML中的用况图和类图描述.生成的软件快速原型将包括用况图中每个用例的执行,用一个系统数据库来描述当前系统状态, 该系统数据库保存了当前系统中存在的所有对象以及对象之间的关系.     

Diagnosability of fair discrete event systems

Failure diagnosability has been widely studied using discrete event system (DES) models. It is, however, shown in this work by means of a counterexample that the diagnosability condition, which has been shown to be necessary and sufficient in the DES context, fails to hold for many real‐world hybrid systems. This is because the abstraction employed in formulating the DES models obliterates the continuous dynamics. In the present work, a new failure diagnosability mechanism has been developed for discrete time hybrid system (DTHS) models to alleviate this problem. A new diagnosability condition is proposed and its necessity and sufficiency with respect to the diagnosability definition are established formally. Finally, the method of A‐diagnosability, which can also be used to circumvent this problem and which needs additional probabilistic information for diagnosability analysis, has been shown to have a higher computational complexity than the DTHS model based method proposed in this paper. Further, it is also highlighted that the DTHS model based diagnosability analysis technique is capable of diagnosing faults that degrade the temporal performance of the system, which cannot be handled by the A‐diagnosability analysis mechanism. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

MPEC strategies for optimization of a class of hybrid dynamic systems

With the development and widespread use of large-scale nonlinear programming (NLP) tools for process optimization, there has been an associated application of NLP formulations with complementarity constraints in order to represent discrete decisions. In particular, these constraints arise frequently in equation-based formulations for real-time optimization. Also known as mathematical programs with equilibrium constraints (MPECs), these formulations can be used to model certain classes of discrete events and can be more efficient than a mixed integer formulation, particularly for large systems with many discrete decisions, such as dynamic systems with switches at any point in time. In this study, we consider and extend MPEC formulations for the optimization of a class of hybrid dynamic models, where the differential states remain continuous over time. These include differential inclusions of the Filippov type. Here, particular care is required in the formulation in order to preserve smoothness properties of the dynamic system. Results on three case studies, including process control examples, illustrate the effectiveness and accuracy of the proposed MPEC optimization methodology for a class of hybrid dynamic systems.