均匀增长无标度网络的等价模型

分析了无标度网络的BA模型和与BA等价的随机连接到边模型.在给出边的权重和节点适应度定义的基础上,提出了无标度网络的边加权网络模型和节点适应度模型.通过解析的方法证明了它们与BA模型的等价性.计算机模拟的结果也说明了它们的等价性,并给出了4个等价模型之间的比较.     

无标度网络的3种病毒控制策略研究

通过定义网络的安全系数S,研究了控制计算机网络病毒传播的度优先免疫、节点介数优先免疫和边介数优先免疫这3种不同的节点优先免疫病毒控制策略.计算结果表明,节点介数优先免疫的病毒控制策略只需对最少的节点进行保护,就能够提供最大的网络安全系数,具有最好的预防效果.     

Ad Hoc网络中基于模拟退火-蚁群算法的QoS路由发现方法

针对Ad Hoc网络的动态网络环境和链路、节点性能限制等不利因素，提出了一种新的QoS路由发现方法——SAANT．该方法利用蚁群算法增加了发现可用QoS路由的概率，利用基于概率的路由转发策略来减少洪泛造成的网络开销，从而强化所提算法的全局搜索能力和自适应性，减小了洪泛对Ad Hoc网络性能的影响．所提方法还利用模拟退火算法调整路由发现算法的搜索方向，以弥补蚂蚁算法收敛速度上的弱点，减少了搜索过程中的停滞现象．在包投递成功率、平均包延迟和吞吐量等方面，通过仿真实验对SAANT、仅基于蚁群算法的QoS路由算法和传统的按需路由算法的方法进行了性能比较，结果表明，在Ad Hoc网络环境下，SAANT的收敛速度、移动性能和网络负载性能均表现出更好的适应性．     

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．     

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。     

复杂动态网络模型研究进展

对复杂动态网络模型的研究现状做了综述,总结了复杂网络的演化机制与属性特征,提出了以复杂动态网络为虚拟计算平台和理论基础,开展大型Ad Hoc网络、传感器网络的网络性能、网络规划、信号增强、拓扑优化的一系列研究课题.     

Techniques of Image Processing Based on Artificial Neural Networks

This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue, saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram, were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.     

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.