The Relationship between Fuzzy Reasoning and Its Temporal Characteristics for Knowledge Management

The knowledge management systems based on artificial reasoning (KMAR) tries to provide computers the capabilities of performing various intelligent tasks for which their human users resort to their knowledge and collective intelligence. There is a need for incorporating aspects of time and imprecision into knowledge management systems, considering appropriate semantic foundations. The aim of this paper is to present the FRTES, a real-time fuzzy expert system, embedded in a knowledge management system. Our expert system is a special possibilistic expert system, developed in order to focus on fuzzy knowledge.Knowledge Management, Artificial Reasoning, predictability

The Combination of Paradoxical, Uncertain, and Imprecise Sources of Information based on DSmT and Neutro-Fuzzy Inference

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this chapter, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT) in the literature, developed for dealing with imprecise, uncertain and paradoxical sources of information. We focus our presentation here rather on the foundations of DSmT, and on the two important new rules of combination, than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout the presentation to show the efficiency and the generality of this new approach. The last part of this chapter concerns the presentation of the neutrosophic logic, the neutro-fuzzy inference and its connection with DSmT. Fuzzy logic and neutrosophic logic are useful tools in decision making after fusioning the information using the DSm hybrid rule of combination of masses.Comment: 20 page

Современная теория управления. Методы синтеза и оптимизации систем управления

Конспект лекций предназначен для освоения студентами новых методов разработки замкнутых систем автоматического регулирования и управления, которые базируются на концепциях обратных задач динамики. Рассматривается управление стохастическими системами, стохастический принцип максимума, оптимальное по быстродействию управление детерминированными и стохастическими системами, а также основы теории нечетких множеств, нечеткой логики, алгоритм синтеза нечетких систем управления и примеры таких систем.Lecture notes are intended for mastering the new methods of developing closed systems of automatic regulation and control, which are based on the concepts of inverse problems of dynamics. Considered control of stochastic systems, stochastic maximum principle, optimal control deterministic and stochastic systems, as well as foundations of the theory of fuzzy sets, fuzzy logic algorithm for the synthesis of fuzzy control systems and examples of such system

An introduction to DSmT

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this introduction, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT), developed for dealing with imprecise, uncertain and conflicting sources of information. We focus our presentation on the foundations of DSmT and on its most important rules of combination, rather than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout this presentation to show the efficiency and the generality of this new approach

Dynamics of Flapping Micro-Aerial Vehicles

[[abstract]]A dynamic-link rule base (DLRB) is introduced to the fuzzy inference systems for the purpose of speeding up and simplifying the fuzzy reasoning. This paper proposes a new reasoning mechanism by adding a dynamic-link rule base between the original rule base and the inference engine. The fuzzy inference system with a dynamic-link rule base is called a dynamic-link-rule-base-fuzzy-inference-system (DLRB-FIS). In the DLRB-FIS, only the fired rules, whose firing strengths are not equal to zero, are included for inference. The mathematical foundations, theorems and architecture of the DLRB-FIS are presented. A numeric example is also given for verifying the practicability of DLRB-FIS. The DLRB-FIS proposed has a general-purpose architecture. Therefore, it can be applied to many kinds of fields, such as fuzzy control, fuzzy image processing, fuzzy decision making, and fuzzy pattern recognition, etc[[conferencetype]]國際[[conferencedate]]20090610~20090612[[iscallforpapers]]Y[[conferencelocation]]St. Louis, US

FLEB: A fuzzy logic e-book

FLEB is an electronic book which attempts to introduce the basic mathematical foundations and applications of fuzzy logic through a software environment which includes images, hypertext, sensitive elements, animations and interactive demos. It also allows executing Xfuzzy, a development tool which eases the description, verification, and synthesis of fuzzy logic-based systems. FLEB, like a usual book, is structured into chapters with pages through which the reader can navigate comfortably. In addition, the information provided can be accessed in a non sequential way thanks to the hypertext and sensitive elements that interconnect linked pages. This capability of non sequential reading together with the exploitation of multimedia software make FLEB a good tool to pedagogically show and explain the basis of fuzzy logic theory and applications.Peer reviewe

FLEB: A fuzzy logic e-book

FLEB is an electronic book which attempts to introduce the basic mathematical foundations and applications of fuzzy logic through a software environment which includes images, hypertext, sensitive elements, animations and interactive demos. It also allows executing Xfuzzy, a development tool which eases the description, verification, and synthesis of fuzzy logic-based systems. FLEB, like a usual book, is structured into chapters with pages through which the reader can navigate comfortably. In addition, the information provided can be accessed in a non sequential way thanks to the hypertext and sensitive elements that interconnect linked pages. This capability of non sequential reading together with the exploitation of multimedia software make FLEB a good tool to pedagogically show and explain the basis of fuzzy logic theory and applications