f—x域内提高地震资料的信噪比

在 f-x 域,地震记录可以表示成有限个调频调幅正弦波的叠合,叠合的结果是一个连续且光滑的函数。我们可以用一个简单的 n 次多项式去逼近这个函数。本文提出了一种在 f-x 域用最小平方拟合原理消除随机噪声的新方法,并证明了在噪声为自噪以及空间采样足够密的条件下,拟合结果与噪声无关,即 f-x 域最小平方拟合处理可以有效地消除记录上的随机白噪,而对有效信号的破坏甚微。由于理论上允许振幅值及同相轴形态在空间上发生变化,因而该方法不具有混波作用,是高分辨率处理的理想工具。     

采用基本门单元完全测试矢量的测试生成算法

文章介绍了一种采用基本逻辑门单元的安全测试矢量集生成测试矢量的方法，该方法可以将搜索空间限制在2(n 1）种组合内。它采用故障支配和故障等效的故障传播、回退等技术，建立了一套从局部到全局的测试生成新方法。同时，利用基本门单元安全测试矢量的规律性，可以实现最小的内存容量要求。在一些基准电路的应用实例中，得到了满意的结果。     

正交多项式的并行算法修正及其稳定性分析

通过修正求解Chebyshev多项式的Clenshaw和Forsythe算法。提出了求解一般正交多项式的两种并行算法。并通过讨论这两种并行算法矩阵形式的舍入误差，对它们的稳定性进行了分析。     

关于Grünwald型多项式算子的线性组合

研究了Grünwald型多项式算子Hn(f;x,r)对f(x)∈Cj[-1,1],1≤j≤r的逼近阶,在连续状态下给出了点态的逼近阶.     

Activity measurement of the constituents in molten Fe–B and Fe–B–C alloys

The distribution ratios of Fe and B between molten Fe–B alloy and molten Ag were measured at temperatures between 1573 and 1923 K. Also, distribution ratios of Fe and B between molten Fe–B–C_satd. alloys and molten Ag were measured at 1873 K. It was found that the excess Gibbs free energy of mixing in molten Fe–B and Fe–B–C alloys can be expressed by utilizing the Redlich–Kister polynomial. The activity curves of the elements in molten Fe–B alloy and Fe–B–C alloy were estimated.     

钢结构稳定设计——理论与实践

本文提出了实际工程结构的非线性理论设计和分析方法(NIDA),并以澳门回归纪念馆的设计为例,展示了本方法的优越性.在设计过程中,设计者无需假设K系数和有效长度,在结构分析完成后便可迅速完成设计,因此本文提出的方法既经济又便利.尽管对于钢框架在往复荷载及动力荷载作用下的弹塑性大位移分析已经非常成熟,但本文的方法开创了结构的非线性分析及设计的新纪元,能够对现行的设计方法理论起到巨大的推动作用.     

非正态随机变量的可靠性评价方法研究

不确定性及可靠性分析技术已广泛应用到水利及土木系统工程分析中，可靠性计算的核心之一是对随机变量进行正态转换。文中旨在对土木工程系统不确定性及可靠性分析中有着潜在应用前景的非正态随机变量的多项式正态转换方法进行研究。通过在各种条件下非正态随机变量的正态多项式转换成果之评价，发现在水利及土木工程系统可靠性分析中常用的许多分布可以保留其自身的比较好的特征。     

Beyond Wiener–Askey Expansions: Handling Arbitrary PDFs

In this paper we present a Multi-Element generalized Polynomial Chaos (ME-gPC) method to deal with stochastic inputs with arbitrary probability measures. Based on the decomposition of the random space of the stochastic inputs, we construct numerically a set of orthogonal polynomials with respect to a conditional probability density function (PDF) in each element and subsequently implement generalized Polynomial Chaos (gPC) locally. Numerical examples show that ME-gPC exhibits both p- and h-convergence for arbitrary probability measures     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.