Cooperative annealing Hopfield network for unconstrained binary quadratic programming problem

The updating rule of the original discrete Hopfield neural network (DHNN) is based on gradient descent dynamics, which always leads to the local minima problem. In this paper, by introducing the idea of the simulated annealing (SA) into the DHNN, we first propose an annealing HNN (AHNN) that permits temporary energy ascent to help the DHNN escape from local minima. Then, from a cooperative perspective, a population of the AHNN processes are simultaneously implemented and coupled by their acceptance probabilities, and thus a cooperative AHNN (CoAHNN) is proposed. The primary objective of the coupling in the CoAHNN is to create cooperative behavior via information exchange among neural networks. This objective helps in the decision of whether uphill moves will be accepted. In addition, coupling can provide information used online to guide the networks toward the global optimum. The CoAHNN is tested on 21 unconstrained binary quadratic programming problems (UBQP) with the size ranging from 3000 to 7000, and 48 maximum cut benchmark problems, a special case of the UBQP, with the size ranging from 512 to 3375. The UBQP consists in maximizing a quadratic 0–1 function. It is a well known NP-hard problem and is considered as a unified model for a variety of combinatorial optimization problems. Simulation results show that the CoAHNN is better than or competitive with other HNN based algorithms, metaheuristic algorithms and state-of-the-art algorithms.     

基于协作Hopfield网络的迭代立体匹配算法

针对立体匹配算法中求解能量函数全局最小问题,提出一种基于协作Hopfield网络的迭代立体匹配算法.它采用两个具有相似结构的Hopfield神经网络协作求解匹配问题,两个网络的不同之处是匹配过程中所采用的基准图不同.然后根据左右一致性约束实现两个Hopfield网络之间的协作,从而避免落入局部最小.为加快收敛速度,该算法将视差图的最优搜索问题转换为二值神经网络的迭代收敛过程.利用局部匹配算法的结果预标记初始视差,以设定神经网络初始权重.并根据局部匹配算法中隐含的假定条件,提出了局部匹配算法视差结果的评估准则,以确定各像素的视差搜索范围,从而减少各次迭代过程中状态待确定的神经元个数.实验表明该方法在性能和收敛速度上都要优于传统的Boltzmann机方法.     

Robust model updating with insufficient data

The increasing need of many industrial fields for highly accurate predictions of performance and reliability gives rise to the need for enhanced underlying mathematical models. The thereby available test data are usually rather limited due to the high costs of experimental measurements. Therefore, decisions have to be made based on limited, incomplete information, which poses a challenging problem.Recently, an approach for coping with insufficient data has been introduced that attempts to extract the information delivered by the data and processes it using few additional assumptions. The underlying distribution is based on an appropriate confidence level providing a safeguard against severe underestimation of the variability of the measured quantities. This method has been applied within the field of statics involving the stochastic identification of one single structural parameter. The present paper shows the extension of this approach to the field of dynamics. It is shown how to deal with insufficient information by applying kernel densities on the stochastic representation of modal data. In addition, the problem of correlation of the established multi-dimensional probability density function will be addressed. As a numerical example the structural dynamics application of the Validation Challenge Workshop has been chosen.     

Properties of Hopfield model with the zero-order synaptic decay

In this paper, we investigate the effect of synaptogenesis on memories in the brain, using the abstract-associative memory model, Hopfield model with the zero-order synaptic decay. Using the numerical simulation, we demonstrate the possibility that synaptogenesis plays a role in maintaining recent memories embedded in the network while avoiding overloading. For the network consisting of 1000 units, it turned out that the minimum decay rate to avoid overloading is 0.02, and the optimal decay rate to maximize the storage capacity is 0.08. We also show that the average numbers of replacement synapses at each learning step corresponding to these two values are 1187 and 21024, respectively.     

Role updating in information systems using model checking

The role-based access control (RBAC) has significantly simplified the management of users and permissions in information systems. In dynamic environments, systems are constantly undergoing changes, and accordingly, the associated configurations need to be updated in order to reflect the systems’ security evolutions. However, such updating process is generally complicated as the resulting system state is expected to meet necessary constraints. This paper presents an approach for assisting administrators to make a desirable update, in light of changes in RBAC systems. We propose a formalization of the update approach, investigate its properties, and develop an updating algorithm based on model checking techniques. Our experimental results demonstrate the effectiveness of the proposed approach.     

Finite element model updating using bees algorithm

In this paper the application of bees algorithm (BA) in the finite element (FE) model updating of structures is investigated. BA is an optimization algorithm inspired by the natural foraging behavior of honeybees to find food sources. The weighted sum of the squared error between the measured and computed modal parameters is used as the objective function. To demonstrate the effectiveness of the proposed method, BA is applied on a piping system to update several physical parameters of its FE model. To this end, the modal parameters of the numerical model are compared with the experimental ones obtained through modal testing. Moreover, to verify the performance of BA, it is compared with the genetic algorithm, the particle swarm optimization and the inverse eigensensitivity method. Comparison of the results indicates that BA is a simple and robust approach that could be effectively applied to the FE model updating problems.     

On the model updating operators in univariate estimation of distribution algorithms

The role of the selection operation—that stochastically discriminate between individuals based on their merit—on the updating of the probability model in univariate estimation of distribution algorithms is investigated. Necessary conditions for an operator to model selection in such a way that it can be used directly for updating the probability model are postulated. A family of such operators that generalize current model updating mechanisms is proposed. A thorough theoretical analysis of these operators is presented, including a study on operator equivalence. A comprehensive set of examples is provided aiming at illustrating key concepts, main results, and their relevance.     

模型修正的正交模型-正交模态改进法

正交模型-正交模态法（CMCM）是一种参数修改的新方法,它具有不依赖于灵敏度分析、不需要进行迭代的特点.但是在有限元存在整体建模误差时,该方法会出现无法完成修正计算的情况,本文针对此问题进行了改进.改进后的方法可以既可以处理存在局部建模误差的情况,也可以处理存在整体建模误差的情况.本文通过梁式结构的数值算例,比较了原修正方法（CMCM）、改进后的修正方法（ICMCM）以及商业软件模型修正FEMtools的修正效果.结果表明：改进的正交模型-正交模态方法可以使分析频率更好地逼近实验值,物理参数的修改也更加准确.     

Damage detection in nonlinear vibrating structures using model updating

Engineering with Computers - This paper presents two distinct model updating strategies for dynamical systems with local nonlinearities based on acceleration time history responses measured...     

HBIA模型中映射同步更新机制研究

提出一种基于HIP协议的间接通信结构模型HBIA,该模型引入了"映射同步更新"机制,将用户的身份信息与位置信息相分离,用接入标识HIT表示终端主机的身份,而广义交换路由标识IP仅用于终端主机在核心网中路由和寻址.主机移动时,由于仅仅改变了其广义交换路由标识,而接入标识并没有发生变化,因此只要及时完成其主、从、子映射的同步更新,原有通信就不会中断,从而有效解决了终端的移动问题.在搭建的环境中进行了实验和验证,测试结果表明,该更新机制能够很好地实现主机的移动并保障了其安全性.     

Convergence of non-autonomous discrete-time Hopfield model with delays

This paper is concerned with boundedness, convergence of solution of a class of non-autonomous discrete-time delayed Hopfield neural network model. Using the inequality technique, we obtain some sufficient conditions ensuring the boundedness of solutions of the discrete-time delayed Hopfield models in time-varying situation. Then, by exploring intrinsic features between non-autonomous system and its asymptotic equations, several novel sufficient conditions are established to ensure that all solutions of the networks converge to the solution of its asymptotic equations. Especially, for case of asymptotic autonomous system or asymptotic periodic system, we obtain some sufficient conditions ensuring all solutions of original system convergent to equilibrium or periodic solution of asymptotic system, respectively. An example is provided for demonstrating the effectiveness of the global stability conditions presented. Our results are not only presented in terms of system parameters and can be easily verified but also are less restrictive than previously known criteria.     

压缩感知跟踪中的特征选择与目标模型更新

目的 为了增强压缩感知跟踪算法在复杂场景下的性能,本文提出了一种特征选择与目标模型更新的改进跟踪方法。方法 提出的方法包含两方面的改进,一是根据特征的正负类条件概率分布的距离选择能有效区分目标与背景的特征;二是根据当前目标与原始目标的差异自适应更新目标外观模型,使得目标遇到较大遮挡或者姿态频繁改变时目标外观模型不会被错误更新。结果 实验表明,对于十个复杂环境下的经典视频序列,本文提出的基于压缩感知的改进跟踪算法在中心误差、成功率和精确度三个指标上均优于最近三个代表性的跟踪算法。结论 提出的新的特征选择和目标模型更新方法,既增强了压缩感知跟踪算法的鲁棒性,又加快了跟踪速度。     

Substructure based approach to finite element model updating

A substructure-based finite element model updating technique is proposed in this paper. A few eigenmodes of the independent substructures and their associated derivative matrices are assembled into a reduced eigenequation to recover the eigensolutions and eigensensitivities of the global structure. Consequently, only the concerned substructures and the reduced eigenequation are re-analyzed in the optimization process, thus reducing the computational load of the traditional model updating methods which perform on the global structure. Applications of the proposed substructure-based model updating to a frame structure and a practical bridge demonstrate that the present method is computationally effective and efficient.     

A partition model approach to updating universal scheme interfaces

The updating of relational databases has received extensive attention in the past few years. However, the various methods proposed present two main drawbacks: either the method cannot perform some update because of nondeterminism, or the method is deterministic but leads to counterintuitive results. In this paper, we use partition semantics to study updating in universal scheme interfaces. It is shown that, contrary to other approaches, the main difficulties are due to nondeterminism of deletions. We characterize when a deletion is deterministic or not, and we show how partition semantics allow for choices in certain cases of nondeterminism     

Online updating appearance generative mixture model for meanshift tracking

This paper proposes an appearance generative mixture model based on key frames for meanshift tracking. Meanshift tracking algorithm tracks an object by maximizing the similarity between the histogram in tracking window and a static histogram acquired at the beginning of tracking. The tracking therefore could fail if the appearance of the object varies substantially. In this paper, we assume the key appearances of the object can be acquired before tracking and the manifold of the object appearance can be approximated by piece-wise linear combination of these key appearances in histogram space. The generative process is described by a Bayesian graphical model. An Online EM algorithm is proposed to estimate the model parameters from the observed histogram in the tracking window and to update the appearance histogram. We applied this approach to track human head motion and to infer the head pose simultaneously in videos. Experiments verify that our online histogram generative model constrained by key appearance histograms alleviates the drifting problem often encountered in tracking with online updating, that the enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our tracking algorithm can infer additional information such as the object poses. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

---

Comparative study of model updating methods using simulated experimental data

The inverse eigensensitivity method and the response function method of analytical model updating have become relatively more popular among other methods and have been successfully applied to the practice of analytical model improvement. This paper gives a detailed comparison of these two approaches of model updating on the basis of computer simulated experimental data with the objective of studying the convergence of the two methods and the accuracy with which they predict the corrections required in a finite element model. The effect of the amount of experimental data used in the process of model improvement on the quality of an updated model is also studied. The test cases of complete, incomplete and noisy experimental data are considered. The updated models are compared on the basis of some error indices constructed to quantify error in the predicted natural frequencies, mode shapes and response functions.     

Distinguishing features of a small Hopfield model with clipping of synapses

To estimate the memory storage as well as the operational reliability of a small Hopfield neural network model with clipping of synapses, the combinatorial methods have been applied. It has been demonstrated that the recognition error probability depends on parity\oddness of etalons number, which causes reliability to decrease sawtooth-like rather than monotonously. It had been also shown that the reliability of the network based on odd number of the etalons can be increased by adding a random etalon.     

系统直和博弈模型下的合作演化

演化博弈论是生物进化论与博弈论结合产生的理论,已成为研究合作演化行为的有力工具.本文研究了基于系统直和博弈模型下的合作演化行为.首先,利用复制者方程分析了双人双策略及三策略对称博弈的演化动力学过程.然后,以石头剪刀布模型和雪堆模型为基础,采用矩阵直和构建系统直和博弈模型,并将所构造的直和矩阵转化为含参数的系统总支付矩阵.随后,说明了这种方法可推广到n个博弈的情形.最后,利用MATLAB对系统直和博弈模型进行仿真模拟,从系统整体的角度分析合作演化.仿真结果表明,混合之后的系统直和博弈较单一博弈而言,合作策略的占比明显增加,且整个系统稳定性更好.这种合作演化机制呈现了全局互惠.     

基于Block-RPLS模型自适应更新的质量预测方法

工业过程包含动态、时变等过程特性.传统的基于PLS方法的质量预测采用的是固定模型,难以实时修正和学习新的过程信息,从而导致建模效率和精度降低,针对该问题提出一种自适应的块式递推偏最小二乘法(Block-RPLS)模型质量预测方法,用于在线调整PLS模型的结构和参数.采用滑动窗方法确定更新的数据块,利用矩阵相似性理论分析窗内数据的结构特性,得到该滑动窗的特征矩阵.同时,引入局部离群因子(LOF)检测滑动窗内离散偏离程度较大的更新数据,通过交叉验证方法修正PLS模型参数自适应学习过程的时变信息.最后,通过数值仿真和青霉素发酵过程的质量预测实验验证所提出方法的有效性.