Evaluation of Quantified Statements using Gradual Numbers - 64

Dr. Ludovic Liétard is currently assistant professor at the University of Rennes 1 (IUT Lannion) in France. His research mainly concerns flexible querying of relational databases using fuzzy set theory and various applications of fuzzy set theory in databases. Dr. Daniel Rocacher is currently assistant professor at the University of Rennes 1 (ENSSAT Lannion) in France. He has proposed new directions to define gradual numbers in the framework of fuzzy set theory. His current research concerns their applications in databases. Evaluation of Quantified Statements using Gradual Numbers -2 -Abstract. This paper is devoted to the evaluation of quantified statements which can be found in many applications as decision-making, expert systems or flexible querying of relational databases using fuzzy set theory. Its contribution is to introduce the main techniques to evaluate such statements and to propose a new theoretical background for the evaluation of quantified statements of type "Q X are A" and "Q B X are A". In this context, quantified statements are interpreted using an arithmetic on gradual numbers from ℕ f , ℤ f and ℚ f . It is shown that the context of fuzzy numbers provides a framework to unify previous approaches and can be the base for the definition of new approaches

Fuzzy SQL queries in standard SQL database

Uncertain queries are very common in many areas of human activity. The problem can be seen particularly in medicine, where expressions like ”very high”, ”low”, ”normal” are commonly used in order to describe different information. However, the most popular data repositories do not allow to form imprecise queries in order to filter information. Therefore, the paper proposes an extension to the standard SQL language allowing anybody to profit from fuzzy database using any SQL engine. The existing approaches employ different mechanisms in order to allow the user to perform fuzzy queries on a database. The most complex solutions modify the database engine itself. However, such approach is strongly bound to the modified server version and must be updated with any development of the original server. Nevertheless, there is possible to store fuzzy information using for instance columns of regular relational database. Therefore, this approach proposes extensions to the query language allowing to use fuzzy information in a query and provides a parser transforming a fuzzy query into a standard SQL. Thus, the database server version is irrelevant. The solution is provided as a module written in multi-platform Java language using popular JDBC database connection

Use of aggregation functions in decision making

A key component of many decision making processes is the aggregation step, whereby a set of numbers is summarised with a single representative value. This research showed that aggregation functions can provide a mathematical formalism to deal with issues like vagueness and uncertainty, which arise naturally in various decision contexts

North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program: 1996

The objectives of the program, which began nationally in 1964 and at JSC in 1965 are to (1) further the professional knowledge qualified engineering and science faculty members, (2) stimulate an exchange of ideas between participants and NASA, (3) and refresh the research and teaching activities of participants' institutions, and (4) contribute to the research objectives of NASA centers. Each faculty fellow spent at least 10 weeks at JSC engaged in a research project in collaboration with a NASA JSC colleague

Machine Learning Methods for Fuzzy Pattern Tree Induction

This thesis elaborates on a novel approach to fuzzy machine learning, that is, the combination of machine learning methods with mathematical tools for modeling and information processing based on fuzzy logic. More specifically, the thesis is devoted to so-called fuzzy pattern trees, a model class that has recently been introduced for representing dependencies between input and output variables in supervised learning tasks, such as classification and regression. Due to its hierarchical, modular structure and the use of different types of (nonlinear) aggregation operators, a fuzzy pattern tree has the ability to represent such dependencies in a very exible and compact way, thereby offering a reasonable balance between accuracy and model transparency. The focus of the thesis is on novel algorithms for pattern tree induction, i.e., for learning fuzzy pattern trees from observed data. In total, three new algorithms are introduced and compared to an existing method for the data-driven construction of pattern trees. While the first two algorithms are mainly geared toward an improvement of predictive accuracy, the last one focuses on eficiency aspects and seeks to make the learning process faster. The description and discussion of each algorithm is complemented with theoretical analyses and empirical studies in order to show the effectiveness of the proposed solutions

Context-Specific Preference Learning of One Dimensional Quantitative Geospatial Attributes Using a Neuro-Fuzzy Approach

Change detection is a topic of great importance for modern geospatial information systems. Digital aerial imagery provides an excellent medium to capture geospatial information. Rapidly evolving environments, and the availability of increasing amounts of diverse, multiresolutional imagery bring forward the need for frequent updates of these datasets. Analysis and query of spatial data using potentially outdated data may yield results that are sometimes invalid. Due to measurement errors (systematic, random) and incomplete knowledge of information (uncertainty) it is ambiguous if a change in a spatial dataset has really occurred. Therefore we need to develop reliable, fast, and automated procedures that will effectively report, based on information from a new image, if a change has actually occurred or this change is simply the result of uncertainty. This thesis introduces a novel methodology for change detection in spatial objects using aerial digital imagery. The uncertainty of the extraction is used as a quality estimate in order to determine whether change has occurred. For this goal, we develop a fuzzy-logic system to estimate uncertainty values fiom the results of automated object extraction using active contour models (a.k.a. snakes). The differential snakes change detection algorithm is an extension of traditional snakes that incorporates previous information (i.e., shape of object and uncertainty of extraction) as energy functionals. This process is followed by a procedure in which we examine the improvement of the uncertainty at the absence of change (versioning). Also, we introduce a post-extraction method for improving the object extraction accuracy. In addition to linear objects, in this thesis we extend differential snakes to track deformations of areal objects (e.g., lake flooding, oil spills). From the polygonal description of a spatial object we can track its trajectory and areal changes. Differential snakes can also be used as the basis for similarity indices for areal objects. These indices are based on areal moments that are invariant under general affine transformation. Experimental results of the differential snakes change detection algorithm demonstrate their performance. More specifically, we show that the differential snakes minimize the false positives in change detection and track reliably object deformations

From n-grams to n-sets: A Fuzzy-Logic-Based Approach to Shakespearian Authorship Attribution.

This thesis surveys the principles of Fuzzy Logic as they have been applied in the last three decades in the micro-electronic field and, in the context of resolving problems of authorship verification and attribution shows how these principles can assist with the detection of stylistic similarities or dissimilarities of an anonymous, disputed play to an author’s general or patterns-based known style. The main stylistic markers are the counts of semantic sets of 100 individual words-tokens and an index of counts of these words’ frequencies (a cosine index), as found in the first extract of approximately 10,000 words of each of 27 well attributed Shakespearian plays. Based on these markers, their geometrical representation, fuzzy modelling and on thee ground of Set Theory and Boolean Algebra, in the core part of this thesis three Mamdani (Type-1) genre-based Fuzzy Expert Systems were built for the detection of degrees (measured on a scale from 0 to 1) of Shakespearianness of disputed and, probably, co-authored plays of the early modern English period. Each of these three expert systems is composed of seven input and two output variables that are associated through a set of approximately 30 to 40 rules. There is a detailed description of the properties of the three expert systems’ inference mechanisms and the various experimentation phases. There is also an indicative graphical analysis of the phases of the experimentation and a thorough explanation of terms, such as partial truths membership, approximate reasoning and output centroids on an X-axis of a two-dimensional space. Throughout the thesis there is an extensive demonstration of various Fuzzy Logic techniques, including Sugeno-ANFIS (adaptive neuro-fuzzy inference system), with which the style of Shakespeare can be modelled in order to compare it with well attributed plays of other authors or plays that are not included in the strict Shakespearian canon of the selected 27 well-attributed, sole authored plays. In addition, other relevant issues of stylometric concern are discussed, such as the investigation and classification of known ‘problem’ and disputed plays through holistic classifiers (irrespective of genre). The results of the experimentation advocate the use of this novel, automated and computer simulation-based method of classification in the stylometric field for various purposes. In fact, the three models have succeeded in detecting the low Shakespearianness of non Shakespearian plays and the results they provided for anonymous, disputed plays are in conformance with the general evidence of historical scholarship. Therefore, the original contribution of this thesis is to define fully functional automated fuzzy classifiers of Shakespearianness. The result of this discovery is that we now know that the principles of fuzzy modelling can be applied for the creation of Fuzzy Expert Stylistic Classifiers and the concomitant detection of degrees of similarity of a play under scrutiny with the general or patterns-based known style of a specific author (in our case, Shakespeare). Furthermore, this thesis shows that, given certain premises, counts of words’ frequencies and counts of semantic sets of words can be employed satisfactorily for stylistic discrimination

Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017

Dieser Tagungsband enthält die Beiträge des 27. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen

Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009

Dieser Tagungsband enthält die Beiträge des 19. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 2.-4. Dezember 2009 im Haus Bommerholz bei Dortmund stattfindet