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。     

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     

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.     

Kripke Semantics, Undecidability and Standard Completeness for Esteva and Godo's Logic MTL∀

The present paper deals with the predicate version MTL of the logic MTL by Esteva and Godo. We introduce a Kripke semantics for it, along the lines of Ono's Kripke semantics for the predicate version of FL_ew (cf. [O85]), and we prove a completeness theorem. Then we prove that every predicate logic between MTL and classical predicate logic is undecidable. Finally, we prove that MTL is complete with respect to the standard semantics, i.e., with respect to Kripke frames on the real interval [0,1], or equivalently, with respect to MTL-algebras whose lattice reduct is [0,1] with the usual order.     

Subjective Situations and Logical Omniscience

The beliefs of the agents in a multi-agent system have been formally modelled in the last decades using doxastic logics. The possible worlds model and its associated Kripke semantics provide an intuitive semantics for these logics, but they commit us to model agents that are logically omniscient. We propose a way of avoiding this problem, using a new kind of entities called subjective situations. We define a new doxastic logic based on these entities and we show how the belief operators have some desirable properties, while avoiding logical omniscience. A comparison with two well-known proposals (Levesque's logic of explicit and implicit beliefs and Thijsse's hybrid sieve systems) is also provided.     

飞行错觉水平评定方法的初步研究

本文从飞行错觉发生的类型和频率出发,尝试对飞行员在实际飞行中所产生的错觉水平或空间定向能力做出基本的评定。本研究采用区间模糊统计的方法,对飞行错觉的类型（倾斜错觉、俯仰错觉、方位错觉、反旋转错觉和倒飞错觉）和频率（从无、偶尔、时有、经常、总是）做了经验的赋值工作,分别计算出了这些模糊概念（除从无外）的心理量表值和模糊度。同时我们认为,飞行错觉水平是频率与类型的函数,其数学表达式为：,并将这一表达式作为评定飞行员飞行错觉水平的基本模型。     

判断与决策研究纵观:基于模糊痕迹理论的视角(英文)

模糊痕迹理论是用于解释记忆、判断与决策的综合性理论,该理论的提出和发展主要基于对信息存储、表征、提取和加工过程的研究。本文首先介绍了模糊痕迹理论的基本原则,在此基础上重点讨论了其要义(gist)如何发挥核心作用,使得模糊痕迹理论有别于其他传统的决策模型。该理论将高级直觉与原始冲动性进行了区分,并且预测决策误差来源于判断与决策的各种不同成分,如背景知识、信息表征、提取和加工过程等。模糊痕迹理论不仅可以解释诸如框架效应、合取谬误等传统决策与判断文献中常讨论的误差现象,同时基于该理论的研究还得到了一些与传统决策理论相悖的新发现。此外,对脑与行为如何发育性变化的研究为我们了解成人的认知过程提供了至关重要的新视角,这些对脑与行为的发育性研究和对特殊人群的研究结果也都支持了模糊痕迹理论对要义加工依赖的预测。     

Special issue: Combining probability and logic

This editorial explains the scope of the special issue and provides a thematic introduction to the contributed papers.     

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.