肯定性“重要度“语义量词的多级估量模糊集模型建立及其应用

在人员选拔和心理素质评价研究中,需要通过被研究者在特定的问题、事件或情境上所作出的评价,来了解他们的心理活动特点。为了确保评价的可靠性,在量表设定上多采用自然语言的回答方式,如是或否,或极重要、很重要、较重要、稍重要等.等级性自然语言可表征人们评价事物的心理量,称语义量词.但这些语义量词不是一个数值,而是一个边界不确定的数量范围,难以作数量化分析,一直是心理测量方法学研究的重要课题。马谋超提出的模糊评判的“多级估量法”［‘l为等级性自然语言的数量化研究提供了重要的理论依据。优秀军事指挥员心理选拔与…     

Computably Enumerable Equivalence Relations

We study computably enumerable equivalence relations (ceers) on N and unravel a rich structural theory for a strong notion of reducibility among ceers.     

Cognitive Factors Affecting Freeze-like Behavior in Humans

Contemporary research on survival-related defensive behaviors has identified physiological markers of freeze/flight/fight. Our research focused on cognitive factors associated with freeze-like behavior in humans. Study 1 tested if an explicit decision to freeze is associated with the psychophysiological state of freezing. Heart rate deceleration occurred when participants chose to freeze. Study 2 varied the efficacy of freezing relative to other defense options and found “freeze” was responsive to variations in the perceived effectiveness of alternative actions. Study 3 tested if individual differences in motivational orientation affect preference for a “freeze” option when the efficacy of options is held constant. A trend in the predicted direction suggested that naturally occurring cognitions led loss-avoiders to select “freeze” more often than reward-seekers. In combination, our attention to the cognitive factors affecting freeze-like behavior in humans represents a preliminary step in addressing an important but neglected research area.     

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.     

Fuzzy Logic Programming and Fuzzy Control

We show that it is possible to base fuzzy control on fuzzy logic programming. Indeed, we observe that the class of fuzzy Herbrand interpretations gives a semantics for fuzzy programs and we show that the fuzzy function associated with a fuzzy system of IF-THEN rules is the fuzzy Herbrand interpretation associated with a suitable fuzzy program.