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.     

The Emotional Dog Gets Mistaken for a Possum.

H. D. Saltzstein and T. Kasachkoff (2004) (see record 2004-21519-004) critique the social intuitionist model (J. Haidt, 2001) (see record 2001-18918-008), but the model they critique is a stripped-down version that should be called the "possum" model. They make 3 charges about the possum model that are not true about the social intuitionist model: that it includes no role for reasoning, that it reduces social influence to compliance, and that it does not take a developmental perspective. After a defense of the honor of the social intuitionist model, this article raises 2 areas of legitimate dispute: the scope and nature of moral reasoning and the usefulness of appealing to innate ideas, rather than to learning and reasoning, as the origin of moral knowledge. This article presents 3 clusters of innate moral intuitions, related to sympathy, hierarchy, and reciprocity. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Extrapolation of fuzzy values from incomplete data bases

This paper presents different approaches which enable a data base management system to obtain a plausible fuzzy estimate for an attribute value of an item for which the information is not explicitly stored in the data base. This can be made either by a kind of analogical reasoning from information about particular items or by means of expert rules which specify the (fuzzy) sets of possible values of the attribute under consideration, for various classes of items. Another kind of expert rules enables the system to compute an estimate from the attribute value of another item provided that, in other respects, this latter item sufficiently resembles the item, the value of which we are interested in; then these expert rules are used either for controlling the analogical reasoning process or for enlarging the scope of application of the first kind of expert rules. The different approaches are discussed in the framework of possibility theory.     

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.     

缺省理论的简化

缺省理论△=(D,W)中的缺省规则集D,是该理论的不确定因素.如果能去掉一些缺省规则而又不影响该理论的延伸,则就在一定程度上简化了该理论.本文证明了关于规范缺省理论的延伸的若干性质,利用这些性质,我们可以把某些规范缺省理论在一定条件下得以化简,有的甚至可以把它们化为一阶逻辑理论.