基于神经网络求解三值模型下的故障诊断问题

在多处理机系统的系统级故障诊断中,一个重要的研究课题是确定最可能故障处理机集,该问题可以归结为NP一完全的整数线性规划问题。连续Hopfietd神经网络能够近似求解最优化问题,因此是解决这类问题的可选路径。文中主要研究如何构建连续Hopfield神经网络,以在三值PMC模型下近似地确定最可能故障集,相比于常用的二值诊断模型,能得到更准确的诊断结果。在超立方体结构上进行了一系列的数值实验,仿真结果表明：该方法具有实用性。     

Agriculture satellite image segmentation using a modified artificial Hopfield neural network

Beekeeping plays an important role in increasing and diversifying the incomes of many rural communities in Kingdom of Saudi Arabia. However, despite the region’s relatively good rainfall, which results in better forage conditions, bees and beekeepers are greatly affected by seasonal shortages of bee forage. Because of these shortages, beekeepers must continually move their colonies in search of better forage. The aim of this paper is to determine the actual bee forage areas with specific characteristics like population density, ecological distribution, flowering phenology based on color satellite image segmentation. Satellite images are currently used as an efficient tool for agricultural management and monitoring. It is also one of the most difficult image segmentation problems due to factors like environmental conditions, poor resolution and poor illumination. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper Hopfield neural network (HNN) is introduced as Pixel clustering based segmentation method for agriculture satellite images.     

Chaos and hyperchaos in a Hopfield neural network

In this paper we investigate numerically the parameter-space of an autonomous system of four nonlinear first-order ordinary differential equations, which represents a Hopfield neural network with four neurons. The study considers three independent two-dimensional cross-sections of the three-dimensional parameter-space generated by this mathematical model, every constructed considering Lyapunov exponent values. We show that is possible to completely characterize the dynamics of the system based in these three plots, which are representative of the three-dimensional parameter-space as a whole.     

基于改进证据理论和神经网络的故障诊断模型*

针对单一故障诊断方法精度低的问题,提出了一种基于D-S证据理论和神经网络相融合的决策层融合故障诊断模型。该方法利用证据理论来处理不精确的、模糊的信息,用神经网络来处理证据理论中的基本可信度分配问题。由于证据理论合成公式无法处理高冲突的证据,提出了一种改进的基于冲突焦元的证据合成规则。该模型在降低决策不确定性的同时大大提高了诊断的精度。最后通过发动机故障诊断实例验证了该模型的有效性。     

时滞诱发的忆阻型Hopfield神经网络的复杂动力学

本文研究含时滞的忆阻型环状Hopfield神经网络的稳定性、Hopf分岔以及复杂振荡模式.根据特征方程根分布情况,获得了系统全时滞稳定条件和与时滞相关的稳定条件.通过数值计算揭示了丰富的动力学现象,如多种周期运动和混沌吸引子等,并给出了Poincaré截面上的分岔图.设计了电路实验平台,取得了与理论分析和数值计算高度吻合的实验结果.     

Partial syndrome-based system-level fault diagnosis using game theory

This paper introduces a novel diagnosis approach, using game theory, to solve the comparison-based system-level fault identification problem in distributed and parallel systems based on the asymmetric comparison model. Under this diagnosis model tasks are assigned to pairs of nodes and the results of executing these tasks are compared. Using the agreements and disagreements among the nodes’ outputs, i.e. the input syndrome, the fault diagnosis algorithm identifies the fault status of the system’s nodes, under the assumption that at most t of these nodes can permanently fail simultaneously. Since the introduction of the comparison model, significant progress has been made in both theory and practice associated with the original model and its offshoots. Nevertheless, the problem of efficiently identifying the set of faulty nodes when not all the comparison outcomes are available to the fault identification algorithm prior to initiating the diagnosis phase, i.e. partial syndromes, remains an outstanding research issue. In this paper, we first show how game theory can be adapted to solve the fault diagnosis problem by maximising the payoffs of all players (nodes). We then demonstrate, using results from a thorough simulation, the effectiveness of this approach in solving the fault identification problem using partial syndromes from randomly generated diagnosable systems of different sizes and under various fault scenarios. We have considered large diagnosable systems, and we have experimented extreme faulty situations by simulating all possible fault sets even those that are less likely to occur in practice. Over all the extensive simulations we have conducted, the new game-theory-based diagnosis algorithm performed very well and provided good diagnosis results, in terms of correctness, latency, and scalability, making it a viable addition or alternative to existing diagnosis algorithms.     

基于离散Hopfield神经网络的数字识别实现

介绍了离散Hopfield神经网络的基本概念;以MATLAB为工具,根据Hopfield神经网络的相关知识,设计了一个具有联想记忆功能的离散型Hopfield神经网络,并给出了设计思路、设计步骤和测试结果。实验结果表明,通过联想记忆,对于带有一定噪声的数字点阵,Hopfield网络可以正确地进行识别,且当噪声强度为0．1时的识别效果较好。     

Validating a neural network application: The case of financial diagnosis

It has been argued that neural network applications should be benchmarked using several data sets of realistic and real problems, and competing algorithms (Prechelt, 1995). However, if applying a neural network model to a particular real problem is in focus, validation should be considered as a suitability evaluation in which several bases of evaluation are combined in a composite judgment. In this paper, five bases of such evaluation are introduced and applied to the validation of a neural network model of financial diagnosis.     

用改进的差分式Hopfield网络实现线性二次型最优控制

为解决差分式Hopfield网络能量函数的局部极小问题,本文对之改进得到一种具有迭代学习功能的线性差分式Hopfield网络.理论分析表明,该网络具有稳定性,且稳定状态使其能量函数达到唯一极小值.基于线性差分式Hopfield网络稳定性与其能量函数收敛特性的关系,本文将该网络用于求解多变量时变系统的线性二次型最优控制问题.网络的理论设计方法表明,网络的稳态输出就是欲求的最优控制向量.数字仿真取得了与理论分析一致的实验结果.     

Stabilization of stochastic Hopfield neural network with distributed parameters

In this paper, the stability of stochastic Hopfield neural network with distributed parameters is studied. To discuss the stability of systems, the main idea is to integrate the solution to systems in the space variable. Then, the integration is considered as the solution process of corresponding neural networks described by stochastic ordinary differential equations. A Lyapunov function is constructed and Ito formula is employed to compute the derivative of the mean Lyapunov function along the systems, with respect to the space variable. It is difficult to treat stochastic systems with distributed parameters since there is no corresponding Ito formula for this kind of system. Our method can overcome this difficulty. Till now, the research of stability and stabilization of stochastic neural networks with distributed parameters has not been considered.     

基于神经网络集成的软件故障预测及实验分析

软件系统故障预测是软件测试过程中软件可靠性研究的重点之一。利用软件系统测试过程中前期的故障相关信息进行建模,预测后期的软件故障信息,以便于后期测试和验证资源的合理分配。根据软件测试过程中已知的软件故障时间序列,利用非齐次泊松分布过程、神经网络、神经网络集成等方法对其进行建模。通过对三个实例分别建模,其预测平均相对误差G-O模型依次为3.02%、5.88%和6.58%,而神经网络集成模型为0.19%、1.88%和1.455%,实验结果表明神经网络集成模型具有更精确的预测能力。     

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。     

Machine cell formation considering processing times and machine capacities: An ortho-synapse Hopfield neural network approach

The machine/part grouping problems can be classified into binary and comprehensive grouping problems depending on whether or not the processing times and the machine capacities are considered. The binary grouping problem arises if the part demands are unknown when the CMS is being developed. If the part demand can be forecast accurately, both the processing times and machine capacities have to be included in the analysis. This gives rise to comprehensive grouping. Both the binary and comprehensive grouping have been proven to be NP-complete which cannot be solved in polynomial time. Considering the large number of parts and machines involved in the industrial design problem, efficient solution methods are highly desirable. In this paper, a new neural network approach (OSHN_g) is proposed to solve the comprehensive grouping problems. The proposed approach has been tested on 28 test problems. The results show that the OSHN_g method is very efficient and its solution quality is comparable to that of a simulated annealing approach.     

Liver stereoscopic visualisation by using Hopfield neural nets

Three dimensional visualisation techniques have been used as a powerful tool in surgical and therapeutic applications. Due to large medical data, huge computations are necessary on 3D visualisation, especially for a real-time system. Many existing methods are sequential, which are too slow to be practical in real applications. In our previous work, we showed boundary detection and feature points extraction by using Hopfield networks. In this paper, a new feature points matching method for 3D surfaces using a Hopfield neural network is proposed. Taking advantage of parallel and energy convergence capabilities in the Hopfield networks, this method is faster and more stable for feature points matching. Stereoscopic visualisation is the display result of our system. With stereoscopic visualisation, the 3D liver used in the experiment can leap out of the screen in true 3D stereoscopic depth. This increases a doctor's ability to analyse complex graphics.     

人工免疫在网络故障诊断中的应用研究

人工免疫系统（AIS）已被广泛的应用在许多领域,如数据分析、多峰函数优化、故障检测等。文章将人工免疫方法引入到PMC模型下网络故障诊断中,文中主要研究如何将AIS应用于系统级故障诊断。理论分析和实验结果表明,基于人工免疫系统的网络故障诊断方法在平均和最差情况下均优于传统的方法。     

A shortest path routing algorithm using Hopfield neural network with an improved energy function

A shortest path routing algorithm using the Hopfield neural network with a modified Lyapunov function is proposed. The modified version of the Lyapunov energy function for an optimal routing problem is proposed for determining routing order for a source and multiple destinations. The proposed energy function mainly prevents the solution path from having loops and partitions. Experiments are performed on 3000 networks of up to 50 nodes with randomly selected link costs. The performance of the proposed algorithm is compared with several conventional algorithms including Ali and Kamoun's, Park and Choi's, and Ahn and Ramakrishna's algorithms in terms of the route optimality and convergence rate. The results show that the proposed algorithm outperforms conventional methods in all cases of experiments. The proposed algorithm particularly shows significant improvements on the route optimality and convergence rate over conventional algorithms when the size of the network approaches 50 nodes.     

基于自联想神经网络的传感器故障诊断

提出一种基于自联想神经网络(AANN)的新算法用于系统中传感器故障诊断。阐述了AANN的结构和算法。具体说明了搜寻2个故障传感器和恢复信号的方法。用改进的AANN诊断有噪声情况下传感器跳变故障并恢复信号。本方法有易实现、结构简单的优点,仿真结果表明:本方法是可行的。     

基于模糊神经网络的平显设备故障诊断研究*

针对平视显示器（HUD）设备自检测和地面故障诊断设备的不足,提出了一种基于模糊BP神经网络的HUD故障诊断方法。简化了故障诊断系统的结构,实现了自动性能检测和故障诊断,能够有效地辨识故障源,隔离了从LRU 级到SRU级的故障,并给出了典型测试项的故障诊断实例。作为一种先进有效的故障诊断技术已经应用于某型飞机HUD和相关系统的故障诊断。     

A Neural Network Appraoch to Fault Diagnosis in Analog Circuits

Thia paper presents a neural network based fault diagnosis approach for analog circuits,taking the tolerances of circuit elements into account.Specifically,a normalization rule of input information,a pseudo-fault domain border(PFDB)pattern selection method and a new output error function are proposed for training the backpropagation(BP) network to be a fault diagnoser.Experimental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy,and provides at least an order-of-magnitude improvement in post-fault diagnostic speed.