图的m—Hamilton性

利用ＬＴＷ序列与邻域交研究图的ｍ－Ｈａｍｉｌｔｏｎ性。     

关于图的分支因子

给出图Ｇ是｛Ｐ２，Ｃｉ｜ｉ≥３｝—消去图的一个充要条件及Ｇ是｛Ｐ２，Ｃｉ｜ｉ≥３｝—覆盖图的一个必要条件和一个充分条件     

基于滑动概率神经网络的早期故障诊断

针对机电设备早期故障难以识别的问题，提出了一种动态的概率密度估计方法——滑动概率神经网络，用以跟踪分析测量信号的概率密度变化过程，及时发现早期故障．该网络以固定不变的抽样集作为第一层，动态滑动的测量信号作为样本层，通过求和层得到抽样集的条件概率密度估计，将样本层内测量信号的概率密度动态地投影到统一的抽样集上．将网络分解成以测量值为中心的子网络，来实现网络的递归运算，并且利用高斯函数的快速衰减特性或使用分段线性函数近似高斯函数，从而提高了网络的计算实时性．通过压缩机喘振过程数据的应用实例，表明该方法能够有效识剐故障的早期征兆．     

基于旅游资源空间结构和市场通达度的旅游区划研究——以江苏沿江旅游发展为例

旅游区划是旅游资源开发的重要工作之一,在进行旅游资源普查、区划、规划之后才能合理的开发利用和保护旅游资源.其中不仅要进行一定数量的旅游资源的调查与评价,还要分析其空间结构和旅游交通网络,确定其旅游中心城市,以体现旅游过程的系统性、完整性以及旅游区的地域性等特点.在目前的旅游区划研究和工作中,对于旅游资源的调查与评价可以依据国家标准《旅游资源分类、调查与评价》,但在分析其空间结构和旅游交通网络方面,还缺少有效的定量分析方法和指标描述.本文基于国家《旅游资源分类、调查与评价》标准对江苏沿江旅游资源进行评价,并应用图论方法对旅游资源进行空间结构分析,以通达度作为描述旅游交通网络的指标,在以上定量研究和综合分析的基础上,对江苏沿江旅游区进行旅游区划研究.     

无线传感器网络研究

介绍了无线传感器网络的体系结构、特点,分析了无线传感器网络研究的一些关键性问题,探讨了无线传感器网络的应用范围。     

The Chromatic Uniqueness of Bipartite Graphs K （ m, n ） - A with ｜ A ｜ = 2

The chromatically uniqueness of bipartite graphs K （m, n） - A（]A] = 2） was studied. With comparing the numbers of partitions into r color classes of two chromatically equivalent graphs, one general numerical condition guaranteeing that K（ m, n） - A （ I A ] = 2） is chromatically unique were obtained. This covers and improves the former correlative results.     

Adaptive control of system with hysteresis using neural networks

1.INTRODUCTIONThe piezoelectric actuators are well suited for micro-position devices in precision engineering because oftheir fast response,nanometer resolution and biggerdriving force[1].However,hysteresis inherent topiezoelectric actuator severely li mits system’s perfor-mance such as giving rise to undesirable accuracy oroscillations,even leading to instability.Hysteresischaracteristics are generally nondifferentiable andusually unknown.It is a difficult task to mitigate itsharmful ef…     

Multicast routing algorithm of multiple QoS based on widest-bandwidth

1 .INTRODUCTIONA mobile Ad hoc network refers to a temporal multi-hop autonomy system,whichis constituted by a groupof mobile nodes containing wireless send-receive equip-ments[1].It possesses manycharacteristics-fast-deploy-able,self-organized,acentric , movable and of multi-hop.It can be wildly applied to many fields ,such asnational defense,emergency and disaster ,scientificin-vestigation and exploration etc .,and uponthat ,it hasgreat prospects[1 ,3 6]. With the wide spread of mo-bile…     

Research on Chaotic Time Series Prediction Based on K-entropy and RBF Neural Networks

In this paper, a method of direct multi-step prediction of chaotic time series is proposed, which is based on Kolmogorov entropy and radial basis functions neural networks. This is done first by reconstructing a phase space using chaotic time series, then using K-entropy as a quantitative parameter to obtain the maximum predictability time of chaotic time series, finally the predicted chaotic time series data can be acquired by using RBFNN. The application considered is Lorenz system. Simulation results for direct multi-step prediction method are compared with recurrence multi-step prediction method. The results indicate that the direct multi-step prediction is more accurate and rapid than the recurrence multi-step prediction within the maximum predictability time of chaotic time series. So, it is convenient to forecast and control with real time using the method of direct multi-step prediction.     

Identify Implicit Communities by Grap Clustering

How to find these communities is an important research work. Recently, community discovery are mainly categorized to HITS algorithm, bipartite cores algorithm and maximum flow/minimum cut framework. In this paper, we proposed a new method to extract communities. The MCL algorithm, which is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm is used to extract communities. By putting mirror deleting procedure behind graph clustering, we decrease comparing cost considerably. After MCL and mirror deletion, we use community member select algorithm to produce the sets of community candidates. The experiment and results show the new method works effectively and properly.