组合数学与数论间的一些交叉问题初探

我们证明了有限域上的一类方程组解的个数与图的顶点着色数有密切关系，而这又对许多着色问题的产生了许多应用。另外，我们也用图论的一些技巧解决了数论中一些问题。     

在WDM环网中疏导率为12的分插复用器数目最小化问题

在WDM网中的一个重要问题是使网络的费用最小化.我们的目的是最小化网络中ADM的个数.这个问题的模型是分拆一个完全图的边成一些子图,使每个子图至多有C条边(这里C是疏导率),并且这些子图的点数之和最小.本文对于给定的C,使用图论和设计理论的工具得到了一些求ADM个数(即A(C,N))的方法.也给出了当C=12并且WDM环网的点数N≡0,16(mod 24)时,问题的最优解(即A(C,N)=N(N-1)/4).     

用Hopfield网络计算约束条件下系统熵的最小值

有约束条件时系统熵的最小值问题是ＮＰ 完全问题，该文利用Ｈｏｐｆｉｅｌｄ人工神经网络解决组合优化问题的能力计算了此问题，得到了较好的结果．     

用Hopfield—型神经网络解四色猜想问题

本文综合讨论了Ｈｏｐｆｉｅｌｄ－型神经网络的迭代算法，使其能保证能量函数单调下降；指出了文［１２］中的错误，并给出了原因；解决了用离散Ｈｏｐｆｉｅｌｄ－型二元神经网络不能求解带有负反馈的问题的难题，大大改进了文［１２］中的算法，能够成功地对任意多个国家的地图用四种颜色着色（使得任意相邻的两个国家着不同的颜色），并可对任意ｋ－可着色问题进行求解．     

Hopfield型模型的收敛性

本文考虑Ｈｏｐｆｉｅｌｄ型模型的收敛性问题和吸引子的吸引域的刻画。证明了Ｎ＜＋∞与Ｎ＝＋∞时Ｈｏｐｆｉｅｌｄ模型在几乎处处的意义下收敛于吸引集。当Ｎ＜＋∞时，模型将在有限时间内稳定于某一吸引子上．对于Ｎ＜＋∞与Ｎ＝＋ ∞的情形，分别得到了吸引域的一个集的刻画。     

关于多层前馈神经网络学习能力的一个注记

给出一种通过适当地选择输入层与隐层间的连接权,来减少单隐层前馈型神经网络隐层节点的个数的方法.应用此方法,分析了具有两个隐层节点的标准单隐层网络的学习能力,并对二元和三元XOR问题中的权值的选择问题进行了详细的讨论.     

拓扑学在模型论中的一个应用

本文利用拓扑学方法研究了ω-范畴理论的性质，并对模型个数问题进行了讨论，获得了ω-范畴的新结果，并得到了关于模型个数的几个结果．     

时滞Hopfield神经网络的全局指数稳定性

利用时滞微分不等式，讨论了时滞Ｈｏｐｆｉｅｌｄ神经网络的全局指数稳定性，获得了几个判定条件。这些结论推广了已知文献中的结果。     

带阈值的Min—max模糊Hopfield网络的稳定性与容错性分析

本文讨论了带阈值的Ｍｉｎ－ｍａｘ模糊Ｈｏｐｆｉｅｌｄ网络的性质，并研究了该网络的一致稳定性与平衡态的Ｌｙａｐｕｎｏｖ稳定性。给出了一个模糊模式成为该网络吸引子的等价条件。然后在一定的条件下，得到了这个网络吸引子的一个非退化的吸引域，从而我们所建立的模糊神经网络模型具有较强的容错性，最后的例子证实了这一点。     

三层前馈型神经网络的最佳逼近研究

本文研究三层前馈型神经网络的最佳逼近能力,我们证明以多项式函数为隐层神经元作用函数的三层前馈型神经网络,当隐层神经元的个数超过某个给定的界限时,网络的输入输出函数张成一有限维线性空间,从而它可以实现对Ｃ（Ｋ）的最佳逼近．并且猜测,对非多项式函数的作用函数,若神经元个数有限,则它不具有最佳逼近性质．     

基于投影算子的回归神经网络模型及其在最优化问题中的应用

研究了一种基于投影算子的神经网络模型．与以前研究投影算子的值域一般是n维欧氏空间中的紧凸子集不同,而是n维欧氏空间中未必有界的闭凸子集,同时目标函数也是一般的连续可微函数,未必为凸函数．证明了所研究的神经网络模型具有整体解轨道,以及当目标函数满足某些条件时解轨道的整体收敛性．此外,还将所研究的模型应用于闭凸约束极小化问题以及非线性互补问题和隐互补问题中,并通过数值模拟说明了该神经网络方法的有效性．     

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.     

On optimal management of resources in distributed networks

In a network of processors, a distributed operating system must handle the management of shared resources. In this paper, it is shown how to solve this problem in using the model previously introduced in [1]. This model (interconnection of N Markov chains each representing locally a distributed process) allows us to prove the good functioning properties for some distributed problems such as the mutual exclusion problem and the deadlock problem, We also prove that fairness is a basic notion for setting the model’s parameters and obtain an optimal working of the network.     

Study on network traffic forecast model of SVR optimized by GAFSA

There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.     

New approach to applying neural network in nonlinear dynamic model

In this work, radial basis function neural network (RBF-NN) is applied to emulate an extended Kalman filter (EKF) in a data assimilation scenario. The dynamical model studied here is based on the one-dimensional shallow water equation DYNAMO-1D. This code is simple when compared with an operational primitive equation models for numerical weather prediction. Although simple, the DYNAMO-1D is rich for representing some atmospheric motions, such as Rossby and gravity waves. It has been shown in the literature that the ability of the EKF to track nonlinear models depends on the frequency and accuracy of the observations and model errors. In some cases, just fourth-order moment EKF works well, but will be unwieldy when applied to high-dimensional state space. Artificial Neural Network (ANN) is an alternative solution for this computational complexity problem, once the ANN is trained offline with a high order Kalman filter, even though this Kalman filter has high computational cost (which is not a problem during ANN training phase). The results achieved in this work encourage us to apply this technique on operational model. However, it is not yet possible to assure convergence in high dimensional problems.     

具有增益的概率网络金融计划模型

本文绘出一类具有增益的概率网络金融计划模型．许多多阶段金融计划问题可纳入这类模型．在这类模型中,随机变量的分布函数与Alexander过滤交易规则密切联系在一起,金融市场交易信号由神经网络产生,目标函数的最优值按其期望值计算．文中提出临界流和临界路的概念,给出目标函数下界等于其期望值的充分必要条件和期望最优解的求解方法．     

Network simplex algorithm for the general equal flow problem

In this paper the general equal flow problem is considered. This is a minimum cost network flow problem with additional side constraints requiring the flow of arcs in some given sets of arcs to take on the same value. This model can be applied to approach water resource system management problems or multiperiod logistic problems in general involving policy restrictions which require some arcs to carry the same amount of flow through the given study period. Although the bases of the general equal flow problem are no longer spanning trees, it is possible to recognize a similar structure that allows us to take advantage of the practical computational capabilities of network models. After characterizing the bases of the problem as good (r+1)-forests, a simplex primal algorithm is developed that exploits the network structure of the problem and requires only slight modifications of the well-known network simplex algorithm.     

Estimating breast cancer risks using neural networks

Breast cancer is one of the most important medical problems. In this paper, we report the results of using neural networks for breast cancer diagnosis. The theoretical advantage is that posterior probabilities of malignancy can be estimated directly, and coupled with resampling techniques such as the bootstrap, distributions of the probabilities can also be obtained. These allow a researcher much more insight into the variability of estimated probabilities. Another contribution is that we present an integrative approach to building neural network models. The issues of model selection, feature selection, and function approximation are discussed in some detail and illustrated with the application to breast cancer diagnosis.     

Permutation-induced acyclic networks for the job shop scheduling problem

In the literature of the combinatorial optimization problems, it is a commonplace to find more than one mathematical model for the same problem. The significance of a model may be measured in terms of the efficiency of the solution algorithms that can be built upon it. The purpose of this article is to present a new network model for the well known combinatorial optimization problem – the job shop scheduling problem. The new network model has similar structure as the disjunctive graph model except that it uses permutations of jobs as decision variables instead of the binary decision variables associated with the disjunctive arcs. To assess the significance of the new model, the performances of exact branch-and-bound algorithmic implementations that are based on both the new model and the disjunctive graph model are compared.     

多式联运分运人选择的模型和算法

在对多式联运分运人选择问题分析的基础上,运用图论技术构建了基于联运分运人选择的多式联运网络。综合考虑了运输的规模经济性、时效性、风险性和联运联盟的稳定性,建立了基于综合运输成本最小、运输风险最小和合作强度最大的多目标优化选择模型。通过主要目标法,将模型转化为单目标模型。借鉴生物免疫原理,设计了基于克隆增扩的人工免疫算法对问题进行求解。最后通过算例对方法进行了验证。