Three-Valued Modal Logic for Qualitative Comparative Policy Analysis with Crisp-Set QCA

Contradictory and missing outcomes are problems common to many qualitative comparative studies, based on the methodology of crisp-set QCA. They also occur in public policy analyses, e.g. if important background variables are omitted or outcomes of new policies are technically censored. As a new solution to these problems, this article proposes the use of three-valued modal logic, originally introduced by the Polish philosopher Jan Lukasiewicz (1970). In addition to true and false, indeterminate is the third truth-value in this alternative approach, which serves to code missing or contradictory data. Moreover, modal operators allow a differentiation between strict and possible triggers and inhibitors of policy outcomes. The advantages of three-valued modal logic in crisp-set QCA are illustrated by an empirical example from comparative welfare policy analysis. Its conclusions allow comparisons with the corresponding results from a conventional crisp-set QCA of the same data-set

Crisp set implementation on video images for the application of surveillance systems

Observing moving objects in far field’s video surveillance is one of the main application areas in computer vision.The strong interest in this research direction is driven by creating full automotive surveillance applications.This paper presents implementing a crisp set on video images in order to evaluate human activities in far field’s surveillance systems.Reducing the storage capacity in surveillance systems is discussed also in this paper.The concept is based on extracting two powerful attributes from objects motion, namely velocity and pixel frequency distribution. This step followed by combining the measurements mentioned above via crisp set rules in order to evaluate the active section in the image plane and to determine the suitable storing rate. The experimental results proved the efficiency of the novel approach

Kvalitatív Komparatív Analízis a pedagógiai térábrázolásban

A tanulmány a pedagógiai terek vizsgálata során empirikus környezetben mutatja be a Kvalitatív Komparatív Analízis (Qualitative Comparative Analysis) csQCA (Crisp-Set QCA) változatát, továbbá a kötetlen reflektív napló, a csQCA és a MAXQDA alkalmazásával megvalósított számítógéppel támogatott kvalitatív adatelemzés közötti kapcsolati rendszert illusztrálja. A kapcsolati háló kiépítése során a tanulmány figyelmet fordít a topologikus fordulat neveléstudomány számára releváns aspektusaira is

Hybrid neural network and fuzzy logic approaches for rendezvous and capture in space

The nonlinear behavior of many practical systems and unavailability of quantitative data regarding the input-output relations makes the analytical modeling of these systems very difficult. On the other hand, approximate reasoning-based controllers which do not require analytical models have demonstrated a number of successful applications such as the subway system in the city of Sendai. These applications have mainly concentrated on emulating the performance of a skilled human operator in the form of linguistic rules. However, the process of learning and tuning the control rules to achieve the desired performance remains a difficult task. Fuzzy Logic Control is based on fuzzy set theory. A fuzzy set is an extension of a crisp set. Crisp sets only allow full membership or no membership at all, whereas fuzzy sets allow partial membership. In other words, an element may partially belong to a set

Fuzzy Galois connections on fuzzy sets

In fairly elementary terms this paper presents how the theory of preordered fuzzy sets, more precisely quantale-valued preorders on quantale-valued fuzzy sets, is established under the guidance of enriched category theory. Motivated by several key results from the theory of quantaloid-enriched categories, this paper develops all needed ingredients purely in order-theoretic languages for the readership of fuzzy set theorists, with particular attention paid to fuzzy Galois connections between preordered fuzzy sets.Comment: 30 pages, final versio

Expert system training and control based on the fuzzy relation matrix

Fuzzy knowledge, that for which the terms of reference are not crisp but overlapped, seems to characterize human expertise. This can be shown from the fact that an experienced human operator can control some complex plants better than a computer can. Proposed here is fuzzy theory to build a fuzzy expert relation matrix (FERM) from given rules or/and examples, either in linguistic terms or in numerical values to mimic human processes of perception and decision making. The knowledge base is codified in terms of many implicit fuzzy rules. Fuzzy knowledge thus codified may also be compared with explicit rules specified by a human expert. It can also provide a basis for modeling the human operator and allow comparison of what a human operator says to what he does in practice. Two experiments were performed. In the first, control of liquid in a tank, demonstrates how the FERM knowledge base is elicited and trained. The other shows how to use a FERM, build up from linguistic rules, and to control an inverted pendulum without a dynamic model