基于交叉层设计的能量负载平衡自组网路由技术

针对已有无线自组网能量平衡路由研究缺少对无线链路信道状态考虑的问题,提出了绕过繁忙区域的能量负载平衡路由技术,采用交叉层设计方法,将M AC层提取的物理信道繁忙程度与节点剩余能量、路径跳数相结合,形成路由度量,并基于改进AODV(ad hoc on dem and d istancevector)路由协议基础上实现路由策略。通过N S-2环境中仿真实验,该技术比传统AODV协议对成功递交分组数、分组成功递交率提高10%的同时,很好地控制了分组延迟的增加,在网络负载增加时甚至更低。     

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.     

智能PID控制器在船舶发电机电压控制中的应用

解决船舶发电机在负荷突变尤其是大负荷突变时能够稳定发电机端电压是控制系统需要解决的基本问题.为此提出和使用一种新型智能PID控制系统,其中PID控制器由三层前馈神经网络组成.利用神经网络的自学习能力,PID控制器的参数能够根据系统动态特性通过神经网络权系数进行自行调整,其特点是结构简单、工作稳定.仿真结果表明,该智能PID控制器比传统的PID控制器具有对扰动响应速度快和具有更好的控制效果和更好的鲁棒性,能更好地稳定船舶发电机端电压.该智能PID控制器已用于船舶发电机控制系统中,并取得满意的效果.     

MANET中的多目标组播路由协议

针对临时性、少量信息群发业务模型,提出一种多目标组播路由协议,利用已有的单播路由信息实现组播发送功能。中间节点根据组播分组中目的节点的地址和路由信息,结合本节点路由表或转发表,对相关目的节点执行转发。协议无需维护组播树和组播成员状态,可在任意提供路由表或转发表的单播路由协议基础上工作。在该应用模型下,协议的分组递交率性能与单播路由协议相当,发送开销比单播和其他组播路由协议大大减少,控制比特开销也较低。     

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.     

一种基于改进径向基神经网络的人脸图像识别方法

常用于径向基神经网络中心参数学习的K-均值聚类算法,易受初始参数选取的影响而收敛于局部极小值.将自动终止聚类判据的减聚类算法用于径向基网络的学习,可根据样本集确定径向基函数数目,且其计算量与数据点的数目与考虑问题的维数无关,很适合于人脸这种维数较高的模式.实验证明,应用这种算法训练径向基神经网络识别人脸,从识别精度到识别速度上都优于传统算法.     

网络科学的理论模型探索及其进展

简要评述了复杂网络分类和研究概况,着重概述了近年来本研究项目在网络科学的理论模型方面的探索及其进展。提出和探索了若干模型:和谐统一的混合择优模型,大统一混合网络模型,量子信息网络模型,纳米相干网络模型,局域世界演化模型,非局域连接模型,社区网络模型,属性连接网络模型,含权科学家合作模型,同步最优网络模型,以及广义同步和集团同步等问题。