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.     

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．     

RLL码的计算机辅助设计

本文着重讨论计算机辅助设计RLL码的具体实现问题.该过程首先根据RLL码两个参数d,k确定一个编码率p/q,然后构造出RLL码的高阶状态转换-输出符号矩阵并求相应的近似特征向量.最后通过状态分裂和状态归并过程,即可得到一个满足d,k约束的编码输出表.其中状态转换-输出符号矩阵为实现计算机辅助设计RLL码提供了有效的数据结构,而且要优化RLL码编码器,就必须有选择地进行状态分裂.     

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.