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。     

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.     

演化硬件平台的数字电路仿真算法

在构建演化硬件的软硬件协同设计平台中,提出了一种数字电路仿真算法。这种仿真算法根据数字电路的拓扑结构,将电路的仿真过程分为两个阶段:第一阶段主要是对数字电路结构化,并将每个宏实体(模块)置于激活或挂起状态;第二阶段则使用两种方法仿真激活的宏实体。此外,根据软硬件协同设计平台的需求,对电路进行优化度和复杂性评价。实验表明这种数字电路仿真算法能较好地满足该系统平台的要求,有效地指导下一步的演化策略。     

一种基于导频的OFDM系统信道估计方法

提出一种使用矩形导频图案的OFDM信道估计方法。接收端首先在频率方向估计信道,然后在时间方向插值。该方法仅需累加和少量乘法运算就能估计出信道冲激响应,复杂度低,实时性高。在未编码16QAM-OFDM系统中,通过仿真将提出的方法与线性、cubic和MMSE插值法进行均方误差和误比特率的比较。结果表明该方法性能优于前两者,接近后者。     

基于图像变换域的双重数字水印方法

基于变换域的双重数字水印方法,通过嵌入鲁棒水印和脆弱水印实现了图像内容版权保护和完整性认证的双重目的.其优点是利用小波变换,分块、适应性嵌入鲁棒水印,在图像的高频和低频中分别嵌入鲁棒水印,使鲁棒水印更加强壮;同时它利用离散余弦变换,在图像的高频部分嵌入脆弱水印,使脆弱水印在遭受攻击或篡改时更易被损坏.     

BP神经网络数值预测方法的研究

人工神经网络作为一个具有高度非线性映射能力的的计算模型,具有广泛的应用前景.在数值预测方面,它不需要预选确定样本的数学模型,仅通过学习样本数据即可以进行预测.本文在对其进行深入研究的基础上,探讨了利用BP神经网络进行数值预测的方法和应该注意的问题,并给出了一些有益的建议.     

The glass transition temperature and microstructure of polyurethane/epoxy resin interpenetrating polymer networks nanocomposites

GLASS TRANSITION TEMPERATURE (TG) HAS BEEN EXTEN- SIVELY STUDIED FOR MANY POLYMERS SINCE IT IS ONE OF THE MOST FUNDAMENTAL FEATURES RELATIVE DIRECTLY TO POLYMER PROPERTIES. IN GENERAL, INORGANIC NANOFILLERS DO NOT DI- RECTLY CONTRIBUTE TO THE TG OF THE MA…