取值于Banach空间中的（E1）囿变函数

引入取值Ｂａｎａｃｈ空间中的（Ｅ１）囿变函数的概念，并讨论它与强、弱囿变函数之间的关系．     

A High Speed Detection Scheme for Point Targets in a Multitarget Environment

The purpose of this paper is to develop a high speed detection scheme for moving and / or stationary point targets in a multitarget environment as registered in an IR image sequence. An iterative approximate 3-D line searching algorithm based upon the geometric representation of lines (for non-maneuvering targets in space) in a 3-D space is derived. The convergency of the algorithm is proved. An analysis is performed of the theoretical detection performance of the algorithm. The statistical experiment results show high effectiveness and computational efficiency of the algorithm in the case of low SNR. The idea may be employed to satisfy the real-time processing requirement of an IR system.     

石油微观渗流数值模拟实验

应用基于骨架隐曲面造型技术，构造了一种抽象的孔隙空间模型，在此基础上运用系统模拟技术开发了一个石油微观运移数值模拟实验平台，用于模拟和展示石油在饱含水孔隙介质中的运移过程，并验证了以浮力为主要运移动力的动力学模型的适用性。     

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.     

弗里德曼空间和宇宙论

利用弗里德曼空间理论，讨论了宇宙学中的一些问题，主要内容：（１）弗里德曼空间的度规张量；（２）弗里德曼空间的能量动量张量；（３）弗里德曼空间的演化和温度的关系；（４）红向移动的理论公式．     

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.     

S桶空间的几个性质(Ⅱ)

讨论了S桶空间在归纳限和稠密子空间方面的一些基本性质．