A new routing algorithm for multirate rearrangeable Clos networks

In this paper, we study the problem of finding routing algorithms on the multirate rearrangeable Clos networks which use as few number of middle-stage switches as possible. We propose a new routing algorithm called the “grouping algorithm”. This is a simple algorithm which uses fewer middle-stage switches than all known strategies, given that the number of input-stage switches and output-stage switches are relatively small compared to the size of input and output switches. In particular, the grouping algorithm implies that m = 2n+(n−1)/2^k is a sufficient number of middle-stage switches for the symmetric three-stage Clos network C(n,m,r) to be multirate rearrangeable, where k is any positive integer and rn/(2^k−1).     

Fault diagnosis of a cstr using fuzzy neural networks

On-line process fault diagnosis using fuzzy neural networks is described in this paper. The fuzzy neural network is obtained by adding a fuzzification layer to a conventional feed forward neural network. The fuzzification layer converts increments in on-line measurements and controller outputs into three fuzzy sets: “increase”, “steady”, and “decrease”. Abnormalities in a process are represented by qualitative increments in on-line measurements and controller outputs. These are classified into various categories by the network. By representing abnormalities in qualitative form, training data can be condensed. The fuzzy approach ensures smooth transitions from one fuzzy sets to another and, hence, robustness to measurement noise is enhanced. The technique has been successfully applied to a CSTR system.     

O(n) routing in rearrangeable networks

In (2n−1)-stage rearrangeable networks, the routing time for any arbitrary permutation is Ω(n²) compared to its propagation delay O(n) only. Here, we attempt to identify the sets of permutations, which are routable in O(n) time in these networks. We define four classes of self-routable permutations for Benes network. An O(n) algorithm is presented here, that identifies if any permutation P belongs to one of the proposed self-routable classes, and if yes, it also generates the necessary control vectors for routing P. Therefore, the identification, as well as the switch setting, both problems are resolved in O(n) time by this algorithm. It covers all the permutations that are self-routable by anyone of the proposed techniques. Some interesting relationships are also explored among these four classes of permutations, by applying the concept of ‘group-transformations’ [N. Das, B.B. Bhattacharya, J. Dattagupta, Hierarchical classification of permutation classes in multistage interconnection networks, IEEE Trans. Comput. (1993) 665–677] on these permutations. The concepts developed here for Benes network, can easily be extended to a class of (2n−1)-stage networks, which are topologically equivalent to Benes network. As a result, the set of permutations routable in a (2n−1)-stage rearrangeable network, in a time comparable to its propagation delay has been extended to a large extent.     

Work-efficient routing algorithms for rearrangeable symmetricalnetworks

The work performed by a parallel algorithm is the product of its running time and the number of processors it requires. This paper presents work-efficient (or cost-optimal) routing algorithms to determine the switch settings for realizing permutations on rearrangeable symmetrical networks such as Benes and the reduced Ω _NΩ_N^-1. These networks have 2n-1 stages with N=2ⁿ inputs/outputs, each stage consisting of N/2 crossbar switches of size (2×2). Previously known parallel routing algorithms for a rearrangeable network with N inputs determine the states of all switches recursively in O(n) iterations using N processors. Each iteration determines the switch settings of at most two stages of the network and requires at least O(n) time on a computer of N processors, regardless of the type of its interconnection network. Hence, the work of any previously known parallel routing algorithm equals at least O(Nn²) for setting up all the switches of a rearrangeable network. The new routing algorithms run on a computer of p processors, 1⩽p⩽N/n, and perform work O(Nn). Moreover, because the range of p is large, the new routing algorithms do not have to be changed in case some processors become faulty     

Semi-tensor product-based algebra-logic mixed representation and fault diagnosis for a class of gate networks

This article studies an algebra-logic mixed representation of gate networks and its application to stuck-at fault diagnosis. First, the gate network is characterized through a logic expression of disjoint sum-of-products, and the system structure of the gate network is described based on 2-to-1 multiplexers. Then, by resorting to the semi-tensor product of matrices, a novel algebra-logic mixed representation is proposed for the gate network through its logic expression and system structure. Furthermore, a novel stuck-at fault diagnosis algorithm for the gate network is presented, where the stuck-at fault testability of the gate network is equivalent to the solution existence of the system of linear equations. Finally, the fault diagnosis of the 4-bit carry look-ahead adder is carried out to demonstrate the effectiveness and feasibility of the proposed theoretical approach and algorithms.     

Fault diagnosis based on adaptive observer for a class of non-linear systems with unknown parameters

In this paper, the fault diagnosis problem for a class of non-linear systems with uncertainty which depends on states, inputs and unknown constant parameters is discussed. Under some geometric conditions, the system is transformed into two different subsystems. One is not affected by actuator faults, so a non-linear adaptive observer can be designed based on the assumption of the strictly positive realness (SPR). The other whose states can be measured is affected by the faults. Actuator fault diagnosis is based on estimations of both the state and the unknown parameters with good accuracy. Discussions on release of SPR requirement and extension to the sensor fault case are also made. Finally, two examples are given in order to illustrate the applicability of the proposed methods for actuator fault diagnosis and sensor fault diagnosis respectively.     

基于参数估计的一类非线性系统故障诊断算法

针对系统模型的不确定性、未知输入扰动和非线性特性, 提出一类非线性系统参数估计的故障诊断算法. 构造系统故障诊断观测器, 采用Lyapunov 稳定性定理验证观测器的稳定性, 通过Barbalat 引理证明满足故障诊断观测器为渐近稳定的表征故障参数的参数估计, 并总结了设计算法流程. 仿真结果表明, 所提出算法具有快速收敛性, 对一类非线性系统诊断效果较好.

     

Fault diagnosis based on high-gain observer with an update law for a class of nonlinear systems

In this paper, by using the well-known high-gain observer design, an update law for the gain and an adaptive estimation of parameters, a new method of fault diagnosis for a class of nonlinear systems is presented. Without resort to any transformation for the parameters, the estimation errors of the states and the parameters are guaranteed to be globally exponentially convergent by a persistent excitation condition. Compared to the existing results, it can be applied to nonlinear systems with nonlinear terms admitting an incremental rate depending on the measured output. A case study further verifies the validity of the proposed research.     

应用人工神经元网络进行pH中和过程故障诊断

应用NeurOn-Line神经元网络应用系统开发技术和G2实时智能专家系统开发技术,开发了一套pH中和过程的故障诊断系统。先简单描述了该pH中和过程及其建模,然后详细论述了该故障诊断系统在NeurOn-Line和G2软件平台上的设计和编程开发情况。共进行了pH中和过程的正常运行模式,pH传感器测量值偏高、pH传感器测量值偏低和碱液浓度变稀三种故障模式的仿真和诊断。仿真结果表明该故障诊断系统能快速准确诊断出pH中和过程的正常运行和故障模式。     

Fault detection for a class of impulsive switched systems

The problem of fault detection for a class of nonlinear impulsive switched systems is investigated in this paper. Fault detection filters are designed such that the augmented systems are stable, and the residual error signal generated by the filters guarantees the H∞ performance for disturbances and faults. Sufficient conditions for the design of fault detection(FD) filters are presented by linear matrix inequalities. Moreover, the filter gains are characterized according to a solution of a convex optimization. Finally, an example derived from a pulse-width-modulation-driven boost converter is given to illustrate the effectiveness of the FD design approach.     

A characterization of solvability for a class of networks

In this paper,we find that Property P can be generalized to characterize the solvability of a kind of networks with any number of sources,thus partially answering the open problem as to whether there are properties similar to Property P to characterize the solvability of some networks.As an application,for a given integer n,we construct such a solvable network that has no solvable solution if its alphabet size is less than n.     

基于神经网络的二次测爆仪传感器故障诊断方法

在全面分析智能二次测爆仪中传感器主要故障的基础上,提出了基于L-M算法的神经网络诊断方案.通过模拟矿井中各种气体体积分数所占比例的仿真实验证明:基于L-M算法的神经网络故障诊断系统具有良好的故障监测与诊断能力.     

Stability analysis for a special class of dynamical networks

In this article, stability analysis and decentralised control problems are studied for a special class of linear dynamical networks. Necessary and sufficient conditions for stability and stabilisability under a decentralised control strategy are given for this type of linear networks. Especially, two types of linear regular networks, star-shaped networks and globally coupled networks, are studied in detail, respectively. A dynamical network can be viewed as a large-scale system composed of some subsystems with some coupling structures, based on this, the relationship between the stability of a network and the stability of its corresponding subsystems is studied. Different from the discussions that the subsystems in networks vary with different coupling structures (Duan, Z.S., Wang, J.Z., Chen, G.R., and Huang, L. (2008), ‘Stability Analysis and Decentralised Control of a Class of Complex Dynamical Networks’, Automatica, 44, 1028–1035), the subsystems in network discussed in this article remain unchanged with different interconnections which is the same as in general large-scale system. It is also pointed out that some subsystems must be made unstable for the whole network to be stable in some special cases. Moreover, the controller design method based on parameter-dependent Lyapunov function is provided.     

基于优化模糊Petri网的矿井提升机故障诊断

为提高矿井提升机故障诊断的准确率,提出了一种基于优化模糊Petri网的矿井提升机故障诊断新方法。该方法首先利用动量BP网络对模糊Petri网的权值、阈值和可信度参数进行优化,然后使用优化好的模糊Petri网模型对矿井提升机进行故障诊断。测试结果表明,该方法对参数优化收敛速度快,故障诊断结果正确率高,有一定的实用价值。     

Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation

This paper presents a fault detection and isolation (FDI) scheme for a class of Lipschitz nonlinear systems with nonlinear and unstructured modeling uncertainty. This significantly extends previous results by considering a more general class of system nonlinearities which are modeled as functions of the system input and partially measurable state variables. A new FDI method is developed using adaptive estimation techniques. The FDI architecture consists of a fault detection estimator and a bank of fault isolation estimators. The fault detectability and isolability conditions, characterizing the class of faults that are detectable and isolable by the proposed scheme, are rigorously established. The fault isolability condition is derived via the so-called fault mismatch functions, which are defined to characterize the mutual difference between pairs of possible faults. A simulation example of a single-link flexible joint robot is used to illustrate the effectiveness of the proposed scheme.     

An algorithm of optimization for a special class of networks

In this paper the author presents an algorithm of optimization for a special class of networks not having the Markov property. A definition of the class of networks under consideration and a formulation of the optimization problem are given. A conception of the algorithm is discussed and next the general and detailed flow diagrams of the algorithm are offered. The realization of the algorithm is illustrated with a simple example showing the process of execution of the tasks included in the algorithm. Some possibilities of applying the algorithm in allocation problems and nonlinear integer programming are presented. The computer program in FORTRAN IV for the execution of the algorithm is enclosed.     

基于多频测试和神经网络的模拟电路故障诊断

多频测试使模拟电路响应的故障状态和正常状态差异最大化,而神经网络具有解决复杂分类问题的能力。结合两者优点,提出一种基于多频测试和神经网络的故障诊断方法:通过灵敏度分析指导多频测试矢量生成,选择最优测试激励;提取各测试节点响应的故障信息,利用神经网络对各种状态下的特征向量进行分类决策,实现对故障元件的检测和定位。实验结果表明,该方法对模拟电路故障诊断非常有效,具有很强的实用性。     

A Bell inequality for a class of multilocal ring networks

Quantum networks with independent sources of entanglement (hidden variables) and nodes that execute joint quantum measurements can create strong quantum correlations spanning the breadth of the network. Understanding of these correlations has to the present been limited to standard Bell experiments with one source of shared randomness, bilocal arrangements having two local sources of shared randomness, and multilocal networks with tree topologies. We introduce here a class of quantum networks with ring topologies comprised of subsystems each with its own internally shared source of randomness. We prove a Bell inequality for these networks, and to demonstrate violations of this inequality, we focus on ring networks with three-qubit subsystems. Three qubits are capable of two non-equivalent types of entanglement, GHZ and W-type. For rings of any number N of three-qubit subsystems, our inequality is violated when the subsystems are each internally GHZ-entangled. This violation is consistently stronger when N is even. This quantitative even-odd difference for GHZ entanglement becomes extreme in the case of W-type entanglement. When the ring size N is even, the presence of W-type entanglement is successfully detected; when N is odd, the inequality consistently fails to detect its presence.