基于非负矩阵理论的同步网络AIMD算法分析

利用非负矩阵理论提出了一个多源竞争共享带宽的网络模型.研究发现,满足模型假设的网络通常都有一个唯一的、全局指数收敛的稳态点.利用这个模型,分析了全局网络收敛到稳态点的收敛速度和稳定性.     

基于神经网络的大规模联立线性方程的实时解

本文研究了大规模线性方程问题的扰动Hopfield神经网络, 并给出了网络收敛的判别准则. 在一定条件下, 网络的稳态误差一致有界或者收敛于0, 网络具有较好的鲁棒性. 最后数值仿真验证了方法的有效性.     

求解二次规划问题的离散时间神经网络的收敛性分析

对求解二次规划问题的离散时间神经网络的收敛性进行了分析,通过选取适当的李雅普诺夫函数给出了网络全局收敛的充分条件,并在该条件下研究了网络的收敛速度,分别对问题的不等式约束左矩阵行满秩和非行满秩的情况进行了讨论,得到了在上述充分条件下对于不等式约束左矩阵行满秩和非行满秩的问题均有网络指数收敛的结论,通过仿真验证了结论的正确性.     

每个节点具有多服务台的Jackson网络流体逼近的收敛速度

研究一个每个节点具有多服务台的Jackson网络.在服务强度为1的条件下,研究了Jackson网络的泛函重对数率与其流体逼近的收敛速度,证明了如果该网络的外部到达过程,服务过程有泛函重对数率,且在流体变换下以指数速度收敛到其相应的流体模型,则其队长过程、负荷过程、忙期过程等也具有相应的性质.     

有向网络上随机—闲聊设置下的一个动量加速算法

本文考虑一个分布式优化问题,其中整个网络上智能体之间的交互可能会发生链路故障,并在随机—闲聊设置下提出了一个有向网络上的动量加速算法.在目标函数是强凸且光滑的假设下,从理论上证明了所提出的算法可以线性收敛到精确解.由于使用了重球动量项,所提出的算法可以更快地收敛到精确解.数值结果表明,与现有的分布式算法相比,该算法能够更快地收敛到精确解,特别是对于病态问题.     

通信噪音条件下非光滑优化问题的分布式derivative-free方法（英文）

本文研究了时变有向图上的非光滑分布式优化.在这样的图中,网络拓扑不仅是强连通的,而且还存在通信噪音.每个节点只能访问其非平滑的局部成本函数.本文给出了一种derivative-free分布式方法来最小化该网络中所有节点的成本函数之和.然后建立了所提出方法的收敛性分析,并获得了收敛速度的显式复杂性界限.当每个局部成本函数都是凸的时,我们的分析表明,所提出的算法以■的速率收敛,收敛速率取决于噪声的上限、光滑参数以及网络信息传播速度和节点间不平衡影响.当每个局部成本函数fi是强凸时,我们得到了O(lnt/t)的更快的收敛速度.最后,用一个数值实验来展示所提出方法的收敛性.     

多类顾客多服务台队列网络的高负荷极限定理

多类顾客多服务台队列网络广泛地应用到计算机网络、通讯网络和交通网络 .由于系统的复杂性 ,其数量指标的精确解很难求出 .为了寻求逼近解 ,本文用概率测度弱收敛理论对进行了研究 ,在高负荷的条件下 ,我们获得了网输入过程、闲时过程和负荷过程的极限定理 .     

变时滞Hopfield神经网络的全局指数稳定性

本文研究了一类具有变时滞Hopfield神经网络的稳定性问题.利用时滞微分不等式方法,获得了几个关于该网络的全局指数稳定性与时滞无关的充分条件,并且给出了此类网络的收敛指数的估计,推广了已知文献的结果.最后给出数值例子证明结论的有效性.     

带有未知内部扰动的星形Euler-Bernoulli梁网络的指数跟踪控制

该文研究了带有未知内部扰动的星形Euler-Bernoulli梁网络的指数跟踪控制问题.首先将该问题等价转化为跟踪网络与被跟踪网络的误差网络的镇定问题.利用滑模控制思想,对误差网络设计了非线性反馈控制方案.通过对状态空间选取适当的范数,运用单调算子理论得到了误差网络的适定性.通过构造适当的Lyapunov函数,证明误差网络按任一收敛率指数稳定.这表明跟踪网络能够按任一给定速率以指数速度跟踪到目标网络.     

一类生物复制网络度分布的重对数律

本文研究一类生物复制网络度分布的收敛速度.利用组合和概率论知识,借助于文[6]中的鞅,讨论了度分布的重对数律.     

A neural network system for solving an assortment problem in the steel industry

A neural network model for solving an assortment problem found in the iron and steel industry is discussed in this paper. The problem arises in the yard where steel plate is cut into rectangular pieces. The neural network model can be categorized as a Hopfield model, but the model is expanded to handle inequality constraints. The idea of a penalty function is used. A large penalty is applied to the network if a constraint is not satisfied. The weights are updated based on the penalty values. A special term is added to the energy function of the network to guarantee the convergence of the neural network which has this feature. The performance of the neural network was evaluated by comparison with an existing expert system. The results showed that the neural network has the potential to identify in a short time near-optimal solutions to the assortment problem. The neural network is used as the core of a system for dealing with the assortment problem. In building the neural networks system for practical use, there were many implementation issues. Some of them are presented here, and the fundamental ideas are explained. The performance of the neural network system is compared to that of the expert system and evaluated from the practical viewpoint. The results show that the neural network system is useful in handling the assortment problem.     

一种简单神经Mealy机的构造

构造一种新型神经Mealy机,神经Mealy机具有一定的学习能力,它主要通过学习来获得(von Newman)计算机结构,可以较好地避免普通计算机那样损毁一条电路就带来灾难性后果的情况.其本质是将递归神经网络通过BP优化算法,对Mealy机进行模拟得到,并通过实验对该网络的学习性能进行研究分析.基于形式文法和自动机的等价性,用神经网络来实现文法推导.先采用神经网络对样本集进行学习,这些样本可由一个经典Mealy机生成,然后从训练完的神经网络提取出自动机.     

基于蚁群神经网络的财务危机预警方法

为了克服神经网络财务危机预警方法收敛慢、不收敛和网络结构难以确定等缺陷,提出了基于蚁群算法的改进神经网络财务危机预警方法。将神经网络模型的结构和参数进行编码,利用蚁群算法确定若干个神经网络模型的结构和参数,然后通过评价函数得到神经网络的最佳结构,最后通过BP算法训练该神经网络,得到神经网络财务危机预警模型。验证结果表明,该模型结构简单、预警精度高。     

Information processing,memories, and synchronization in chaotic neural network with the time delay

Information processing and two types of memory in an analog neural network model with time delay that produces chaos similar to the human and animal EEGs are considered. There are two levels of information processing in this neural network: the level of individual neurons and the level of the neural network. Similar to the state of brain, the state of chaotic neural network is defined. It is characterized by two types of memories (memory I and memory II) and correlation structure between the neurons. In normal (unperturbed) state, the neural network generates chaotic patterns of averaged neuronal activities (memory I) and patterns of oscillation amplitudes (memory II). In the presence of external stimulation, the activity patterns change, showing changes in both types of memory. As in experiments on stimulation of the brain, the neural network model shows synchronization of neuronal activities due to stimulus measured by Pearson's correlation coefficient. An increase in neural network asymmetry (increase of the neural network excitability) leads to the phenomenon similar to the epilepsy. Modeling of brain injury, Parkinson's disease, and dementia is performed by removing and weakening interneuron connections. In all cases, the chaotic neural network shows a decrease of the degree of chaos and changes in both types of memory similar to those observed in experiments with healthy human subjects and patients with Parkinson's disease and dementia. © 2005 Wiley Periodicals, Inc. Complexity 11:39–52, 2005     

A simplified recurrent neural network for pseudoconvex optimization subject to linear equality constraints

In this paper, the optimization techniques for solving pseudoconvex optimization problems are investigated. A simplified recurrent neural network is proposed according to the optimization problem. We prove that the optimal solution of the optimization problem is just the equilibrium point of the neural network, and vice versa if the equilibrium point satisfies the linear constraints. The proposed neural network is proven to be globally stable in the sense of Lyapunov and convergent to an exact optimal solution of the optimization problem. A numerical simulation is given to illustrate the global convergence of the neural network. Applications in business and chemistry are given to demonstrate the effectiveness of the neural network.     

数据挖掘在房地产价格预测中的应用研究

通过基于数据挖掘理论的粗糙集和神经网络的研究,用属性约简算法约简并提取了影响房地产价格的主要指标因素,对降维后的数据进行网络学习和训练,最后用训练好的的网络检验测试样本.方法使学习训练的速度和识别率提高了,为房地产价格预测提供了一种更为有效和实用的新途径.     

A Neural Network Application in Personnel Scheduling

In this paper, we present and evaluate a neural network model for solving a typical personnel-scheduling problem, i.e. an airport ground staff rostering problem. Personnel scheduling problems are widely found in servicing and manufacturing industries. The inherent complexity of personnel scheduling problems has normally resulted in the development of integer programming-based models and various heuristic solution procedures. The neural network approach has been admitted as a promising alternative to solving a variety of combinatorial optimization problems. While few works relate neural network to applications of personnel scheduling problems, there is great theoretical and practical value in exploring the potential of this area. In this paper, we introduce a neural network model following a relatively new modeling approach to solve a real rostering case. We show how to convert a mixed integer programming formulation to a neural network model. We also provide the experiment results comparing the neural network method with three popular heuristics, i.e. simulated annealing, Tabu search and genetic algorithm. The computational study reveals some potential of neural networks in solving personnel scheduling problems.     

Global exponential convergence and stability of gradient-based neural network for online matrix inversion

Wang proposed a gradient-based neural network (GNN) to solve online matrix-inverses. Global asymptotical convergence was shown for such a neural network when applied to inverting nonsingular matrices. As compared to the previously-presented asymptotical convergence, this paper investigates more desirable properties of the gradient-based neural network; e.g., global exponential convergence for nonsingular matrix inversion, and global stability even for the singular-matrix case. Illustrative simulation results further demonstrate the theoretical analysis of gradient-based neural network for online matrix inversion.     

一种利用神经网络改善判断矩阵一致性的方法

针对层次分析法中判断矩阵致性改进问题,提出了一种利用神经网络改善判断矩阵一致性的方法.本文在建立了BP神经网络模型的基础上,把判断矩阵一致性调整问题转化为BP神经网络的多输入多输出求解问题.经BP神经网络算法调整过的判断矩阵再返回给专家进一步调整使其符合萨迪标度.计算实例表明,此种方法是可行的.     

基于自组织特征映射神经网络的生态城市分类

神经网络用于样本分类是一个新的研究课题,本文利用自组织特征映射神经网络,对生态城市进行分类.计算实例表明,用自组织特征映射神经网络用于分类是准确和可靠的.