神经网络自适应滤波器在故障诊断中的应用

分析了用自适应滤波器进行故障诊断的方法．针对该方法在计算上要花费大量时间、难于完成实时诊断任务的缺点，提出了用神经网络自适应滤波器来完成故障诊断的方法．神经网络有极快的运行速度，能很好地完成实时诊断任务．     

希尔伯特变换器优化设计研究

提出了一种基于神经网络算法的希尔伯特变换器优化设计方法，证明了神经网络算法的收敛性，提出了希尔伯特变换器的优化设计实例。仿真结果表明了该网络模型是高效的神经网络模型。     

基于RBF网络的增量学习

提出了一种基于径向网络的增量学习算法 ,在对新样本进行学习时 ,通过恢复受干扰样本的方式来尽可能保持已有的知识不变 .模拟实验证明了本算法的正确性和有效性 .     

基于神经网络的建筑行业投标报价研究

在建筑行业的投标报价过程中，如何在众多的信息中选出对最终报价影响较大的几项因素以及如何确定这些因素与最终报价之间的关系是一个棘手的问题．论文针对这两大难题提出了基于神经网络技术的解决方法．首先根据贡献变量分析理论确定出影响报价结果的9个报价因素，从而建立起基于神经网络的报价模型，然后在所确定模型的基础上改进传统的BP算法，进一步提高网络的泛化能力．从实际应用的结果可以看出，经过变量选择后所确定的报价因素是合理的，改进学习算法后的网络的泛化能力也有了很大的提高．     

Non-Minimum Phase Nonlinear System Predictive Control Based on Local Recurrent Neural Networks

Abstract: After a recursive multi-step-ahead predictor for nonlinear systems based on local recurrent neural networks isintroduced, an intelligent PID controller is adopted to correct the errors including identified model errors and accumulatederrors produced in the recursive process. Characterized by predictive control, this method can achieve a good controlaccuracy and has good robustness. A simulation study shows that this control algorithm is very effective.     

Petri网络在无控制交叉口延误分析中的应用

选用比较适合描述系统组织结构和动态行为的Petri网络模型对主路优先通行的无信号交叉口运行过程建立HSPN模型．在此基础上，结合间隙接受理论，对无控制交叉口进行平均延误分析探索，为交叉口仿真控制软件的研发奠定了算法基础。     

Discovering Frequent Subtrees from XML Data Using Neural Networks

By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML has been available on Internet. In this paper, we study a data-mining problem of discovering frequent ordered sub-trees in a large collection of XML data, where both of the patterns and the data are modeled by labeled ordered trees. We present an efficient algorithm of Ordered Subtree Miner （OSTMiner） based on two- layer neural networks with Hebb rule, that computes all ordered sub-trees appearing in a collection of XML trees with frequent above a user-specified threshold using a special structure EM-tree. In this algo- rithm, EM-tree is used as an extended merging tree to supply scheme information for efficient pruning and mining frequent sub-trees. Experiments results showed that OSTMiner has good response time and scales well.     

包含式完全正则(T*3(1/2))是相对可乘的

本文讨论包含式完全正则空间（T31/2^＊）关于相对积拓扑的可乘性问题．     

Efficient and power-awareness query over sensor networks

Sensor networks consisted of low-cost, low-power, multifunctional miniature sensor devices have played an important role in our daily life. Light and humidity monitoring, seismic and animal activity detection, environment and habitat monitoring are the most common applications. However, due to the limited power supply, ordinary query methods and algorithms can not be applied on sensor networks. Queries over sensor networks should be power-aware to guarantee the maximum power savings. The minimal power consumption by avoiding the expensive communication of the redundant sensor nodes is concentrated on. A lot of work have been done to reduce the participated nodes, but none of them have considered the overlapping minimum bounded rectangle (MBR) of sensors which make them impossible to reach the optimization solution. The proposed OMSI-tree and OMR algorithm can efficiently solve this problem by executing a given query only on the sensors involved. Experiments show that there is an obvious improvement compared with TinyDB and other spatial index, adopting the proposed schema and algorithm.