基于遗传算法的模糊综合评价在心理测量中的应用

提出了运用模糊数学对利克特量表数据进行分析的方法, 探讨了人们在进行模糊综合评价时, 所采用的算子和对各个自变量的权重分配, 并且运用遗传算法(GA)来确定相关的权重。以大学生对康师傅红烧牛肉面的评价数据为例, 运用基于遗传算法的模糊综合评价方法, 发现男生采用了“最大最小”合成算子, 女生采用了“有界和、取小”合成算子。研究结果表明, 基于遗传算法的模糊综合评价方法可以对利克特量表的心理测量数据进行有效的分析。     

Negativity bias in false memory: moderation by neuroticism after a delay

The negativity bias is the tendency for individuals to give greater weight, and often exhibit more rapid and extreme responses, to negative than positive information. Using the Deese-Roediger-McDermott illusory memory paradigm, the current study sought to examine how the negativity bias might affect both correct recognition for negative and positive words and false recognition for associated critical lures, as well as how trait neuroticism might moderate these effects. In two experiments, participants studied lists of words composed of semantic associates of an unpresented word (the critical lure). Half of the lists were comprised of positive words and half were comprised of negative words. As expected, individuals remembered negative list words better than positive list words, consistent with a negativity bias in correct recognition. When tested immediately (Experiment 1), individuals also exhibited greater false memory for negative versus positive critical lures. When tested after a 24-hr delay (Experiment 2), individuals higher in neuroticism maintained greater false memory for negative versus positive critical lures, but those lower in neuroticism showed no difference in false memory between negative and positive critical lures. Possible mechanisms and implications for mental health disorders are discussed.     

Multi-layer architecture for adaptive fuzzy inference system with a large number of input features

The Sugeno adaptive fuzzy neural network using training data is a good approximation to model different systems. The large number of adaptive neuro-fuzzy inference system (ANFIS) input features is a major challenge in using ANFIS and is not applicable with increased parameters. We present a solution for many input features solving modular problems; we created a multi-layer architecture of SUB-ANFIS (MLA-ANFIS) for this purpose. Different topologies were created with various combinations of multiple input features, and an error indicator was calculated for each combination of topologies. Finally, the best topology was chosen among the states with the highest possible performance. We implemented a multi-layered approach based on 365-day concrete compressive strength data with eight input features and the optimized MLA-ANFIS topology (5-3-1) for this purpose from different ANFIS topologies and neural networks. Finally, the results from five other datasets prove the impact of the proposed MLA-ANFIS approach compared to the neural network method.     

Resource-origins of Nonmonotonicity

Formal nonmonotonic systems try to model the phenomenon that common sense reasoners are able to “jump” in their reasoning from assumptions Δ to conclusions C without their being any deductive chain from Δ to C. Such jumps are done by various mechanisms which are strongly dependent on context and knowledge of how the actual world functions. Our aim is to motivate these jump rules as inference rules designed to optimise survival in an environment with scant resources of effort and time. We begin with a general discussion and quickly move to Section 3 where we introduce five resource principles. We show that these principles lead to some well known nonmonotonic systems such as Nute’s defeasible logic. We also give several examples of practical reasoning situations to illustrate our principles. Edited by Hannes Leitgeb     

MV-Algebras and Quantum Computation

We introduce a generalization of MV algebras motivated by the investigations into the structure of quantum logical gates. After laying down the foundations of the structure theory for such quasi-MV algebras, we show that every quasi-MV algebra is embeddable into the direct product of an MV algebra and a “flat” quasi-MV algebra, and prove a completeness result w.r.t. a standard quasi-MV algebra over the complex numbers. Presented by Heinrich Wansing     

Component Models for Fuzzy Data

The fuzzy perspective in statistical analysis is first illustrated with reference to the “Informational Paradigm” allowing us to deal with different types of uncertainties related to the various informational ingredients (data, model, assumptions). The fuzzy empirical data are then introduced, referring to J LR fuzzy variables as observed on I observation units. Each observation is characterized by its center and its left and right spreads (LR₁ fuzzy number) or by its left and right “centers” and its left and right spreads (LR₂ fuzzy number). Two types of component models for LR₁ and LR₂ fuzzy data are proposed. The estimation of the parameters of these models is based on a Least Squares approach, exploiting an appropriately introduced distance measure for fuzzy data. A simulation study is carried out in order to assess the efficacy of the suggested models as compared with traditional Principal Component Analysis on the centers and with existing methods for fuzzy and interval valued data. An application to real fuzzy data is finally performed. We would like to express our gratitude to the Editor, the Associate Editor, and the Referees whose comments and suggestions improved significantly the quality of the paper.     

On Vagueness,Truth Values and Fuzzy Logics

Some aspects of vagueness as presented in Shapiro’s book Vagueness in Context [23] are analyzed from the point of fuzzy logic. Presented are some generalizations of Shapiro’s formal apparatus. Presented by Daniele Mundici     

网络旗帜广告的记忆效果

由于人们对网络旗帜广告效果的质疑,在网络广告的计费方式上也出现了两种计价模式,分别为按照浏览率和点击率来计量广告效果。其本质问题在于单纯浏览是否能够产生广告效果,以及浏览和点击各自所能带来的广告效果差异有多大。本研究利用模糊测量及相应统计方法,就上网者对网络旗帜广告的记忆效果进行测量。结果显示,在单纯浏览的情况下,上网者对网络旗帜广告已经留有印象,但这种记忆效果并不随着浏览次数的增加而显著改善;点击所能带来的广告记忆效果约为单纯浏览对应效果的1.4~1.5倍     

Relevance and argumentation: How bald can you get

This paper is concerned with vagueness in language, its relation to logico-philosophical questions on the one hand, and to so-called syncategorematic terms and their linguistic use on the other hand. It attempts to show that it is not language itself which is vague but rather the way we use it.     

Comparing Latent Structures of the Grade of Membership,Rasch, and Latent Class Models

This paper focuses on model interpretation issues and employs a geometric approach to compare the potential value of using the Grade of Membership (GoM) model in representing population heterogeneity. We consider population heterogeneity manifolds generated by letting subject specific parameters vary over their natural range, while keeping other population parameters fixed, in the marginal space (based on marginal probabilities) and in the full parameter space (based on cell probabilities). The case of a 2 × 2 contingency table is discussed in detail, and a generalization to 2^J tables with J ≥ 3 is sketched. Our approach highlights the main distinction between the GoM model and the probabilistic mixture of classes by demonstrating geometrically the difference between the concepts of partial and probabilistic memberships. By using the geometric approach we show that, in special cases, the GoM model can be thought of as being similar to an item response theory (IRT) model in representing population heterogeneity. Finally, we show that the GoM item parameters can provide quantities analogous to more general logistic IRT item parameters. As a latent structure model, the GoM model might be considered a useful alternative for a data analysis when both classes of extreme responses, and additional heterogeneity that cannot be captured by those latent classes, are expected in the population. This work was supported by Award #1R03 AG18986-01 from the National Institute on Aging and NIH grant #1R01 CA94212-01. The presentation of the ideas in this paper owes much to discussions with Stephen Fienberg and Brian Junker, Carnegie Mellon University. The author thanks Jim Ramsay and two anonymous reviewers for their valuable comments on earlier drafts of this paper.