Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

透水坡面降雨径流过程的运动波近似解析解

本文从坡面流运动波理论的基本方程出发，利用运动波特征线法和分级叠加法，推导出了实验条件下考虑净雨强随时间变化的坡面流运动波方程近似解析解。用实验实测资料对模拟计算结果进行了验证。结果表明，坡面漫流过程的模拟计算结果与实测资料符合良好。     

一种有效的并行汉字／字符串相似检索技术

本文提出了一种有效的并行汉字／字符串相似检索技术．通过引入搜索状态向量及字符一模式匹配向量，该技术将字符串匹配比较转化为简单的整数字位运算，通过对字符串方向相反的搜索有效地实现了多处理机对汉字／字符串的并行相似检索．文中也给出了并行实现算法，同时分析了算法的复杂性．     

FILIP: A fuzzy intelligent information system with learning capabilities

FILIP (fuzzy intelligent learning information processing) system is designed with the goal to model human information processing. The issues addressed are uncertain knowledge representation and approximate reasoning based on fuzzy set theory, and knowledge acquisition by “being told” or by “learning from examples”. Concepts that can be “learned” by the system can be imprecise (fuzzy), or the knowledge can be incomplete. In the latter case, FILIP uses the concept of similarity to extrapolate the knowledge to cases that were not covered by examples provided by the user. Concepts are stored in the Knowledge Base and employed in intelligent query processing, based on flexible inference that supports approximate matches between the data in the database and the query.

The architecture of FILIP is discussed, the learning algorithm is described, and examples of the system's performance in the knowledge acquisition and querying modes, together with its explanatory capabilities are shown.     

QFT control design for an approximately linearized pneumatic positioning system

A block‐oriented approximate feedback linearization for control of a pneumatic cylinder positioning system is introduced and a rather detailed discussion is presented on the uncertain linearization residual characterization. It is shown that making use of the characterized gain–phase information of the linearized system leads to a more reasonable trade‐off between performance and stability in the QFT control design and thus results in high control performance. Simulation and experimental results are shown. Copyright © 2003 John Wiley & Sons, Ltd.     

On solving systems of ordinary differential equations on MIMD-computers

The error of solution of Cauchy problems for systems of ordinary differential equations is estimated in the case where the input data are approximate. It is shown how to prepare a program for computing the right-hand sides of the system automatically and simultaneously. Diagrams are presented to illustrate the efficiency of parallelization. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 175–182, March–April 2007.     

一种前馈型全摆幅恒跨导准恒增益CMOS运放设计

设计了一种适合在低电源电压下工作的前馈型输入级放大结构，在全摆幅的动态工作范围内，输入级跨导保持不变，采用负载电流补偿以保证增益近似恒定，输出采用前馈型AB类输结构，实现全摆幅输出。     

滑移线场近似作图法的原理

给出了滑移线场近似作图法的原理、证明以及近似滑移线场的特点，为进一步利用计算机求解应力分布和变形规律，尤其是为近似滑移线的计算机绘制奠定了基础．