PEAK COVARIANCE STABILITY OF A RANDOM RICCATI EQUATION ARISING FROM KALMAN FILTERING WITH OBSERVATION LOSSES

We consider the stability of a random Riccati equation with a Markovian binary jump coefficient. More specifically, we are concerned with the boundedness of the solution of a random Riccati difference equation arising from Kalman filtering with measurement losses. A sufficient condition for the peak covariance stability is obtained which has a simpler form and is shown to be less conservative in some cases than a very recent result in existing literature. Furthermore, we show that a known sufficient condition is also necessary when the observability index equals one.     

也谈提高电力供电质量的有效方法

电力企业为提高供电质量和服务水平，需要一套用电倜电能管理系统。本文主要介绍低压配电监控装置的应用，它是提高电力供电质量的有效方法。     

东露天铁路专线采空区治理对策研究

针对东露天铁路专线所经过的采空区特点,分析了典型采空区上覆岩层的稳定性和上覆岩层移动形式,提出该地区采空区的治理措施。     

无缝线路稳定性探讨

从无缝线路稳定性研究意义、研究理论着手,分析探讨了影响稳定性的因素,提出提高无缝线路稳定性的措施。     

一种由负定函数dV（x）／dt判定动态系统原点稳定性的方法

提出一种由负定导函数判定动态系统稳定性的方法，给出在已知导函数为负定的情况下系统稳定或者不稳定的充要条件，以及一组使用该方法的例子。     

杉木基因型遗传稳定性的空间模型分析

对来自南方１４个杉木种子园的１１２个优良家系在８个测试点进行了生长的遗传稳定性评定，在材积ＡＭＭ１模型分析基础上所作的空间分析结果表明，参试家系在生长性能和遗传稳定性方面有着明显的差异，但具广播稳定性且生长表现卓越的家系所占的比例较少，并且大多来自广西区种子园，因此在作家优良家系选择和推广时，必须先试验后推广，因地制宜。     

价格本体系统投入产出模型中直接消耗系数对临界平均毛利率的影响分析

针对价格本体系统稳定性建立了基本价格投入产出模型,给出了定价稳定性的充要条件和临界平均毛利率的表达式,并研究降低直接消耗系数对该临界平均毛利率的定量影响,得出降低直接消耗系数可提高临界平均毛利率定量关系的结论,并给出了临界平均毛利率的增加幅度.     

Samples Selection for Artificial Neural Network Training in Preliminary Structural Design

An artificial neural network (ANN) is applied in the preliminary structural design of reticulated shells. Major efforts are made to enhance the generalization ability of networks through well-selected training samples. Number-theoretic methods (NTMs) are adopted to generate samples with low discrepancy, i.e.uniformly scattered in the domain, where discrepancy is a quantitative measurement of the uniformity. The discrepancy of the NTM-based sample set is 1/6-1/7 that of samples with equal spacing. In a case study,networks trained by NTM-based samples are compared with those trained by equal-spaced samples in generalizing performance. The results show that both the computational precision and stability of the former ANNs are more satisfactory than those of the latter. It is concluded that the flexibility of ANNs in generalizing can be effectively increased by use of uniformly distributed training samples rather than simply piling data.More reliable uniformity should be obtained, however, through NTMs instead of equal-spaced samples.