Intelligent Counting Under Information Imprecision

In short, this book summarizes the recent contributions of the authors on the stability analysis of the FMB control systems.Detailed analysis and derivation steps are given to enhance the readability, enabling the readers who are unfamiliar with the FMB control systems to follow the materials easily. A wide variety of continuous-time nonlinear control systems such as state-feedback, switching, time-delay and sampled-data FMB control systems, are covered.

In probability and statistics we often have to estimate probabilities and parameters in probability distributions using a random sample. Fuzzy random variables have fuzzy distributions. Instead of using a point estimate calculated from the data we propose using fuzzy numbers which are constructed from a set of confidence intervals..

How do we model the multiple objective decision making problems under fuzzy-like environments? How do you deal with these models? What are the fuzzy-like environments? Fuzzy-Like Multiple Objective Decision Making In order to answer these questions, this book provides an up-to-date methodology system for fuzzy-like multiple objective decision making, which includes modelling system, model analysis system, algorithm system and application system in structure optimization problem, selection problem, purchasing problem, inventory problem, logistics problem and so on.Decision makers usually face multiple, conflicting objectives and the complicated fuzzy-like environments in the real world..

. Multicriteria and Multiagent Decision Making with Applications to Economics and Social Sciences This necessitates the confluence of several research fields, such as foundations of social choice and decision making, mathematics, complexity, psychology, sociology and economics.The book provides a comprehensive and timely report on the topic of decision making and decision analysis in economics and the social sciences.

This book addresses engineers, scientists, professors, students and post-graduate students, who are interested in and work with fuzzy clustering and its applications   The proposed approach can be used for solving different classification problems.A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications Moreover,  a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani’s fuzzy inference systems is introduced.

      Every day decision making and decision making in complex human-centric systems are characterized by imperfect decision-relevant information.

.

  The monograph is the first one showing and emphasizing that the presented methods of intelligent counting are human-consistent: are reflections and formalizations of real, human counting procedures performed under imprecision and, possibly, incompleteness of information. Formally, this collapses to counting in fuzzy sets, interval-valued fuzzy sets or I-fuzzy sets (Atanassov's intuitionistic fuzzy sets).   The whole presentation is self-contained, systematic, and equipped with many examples, figures and tables.

Linguistic Fuzzy Logic Methods in Social Sciences The modern origin of fuzzy sets, fuzzy algebra, fuzzy decision making, and “computing with words” is conventionally traced to Lotfi Zadeh’s publication in 1965 of his path-breaking refutation of binary set theory. Contrary to my expectation, fuzzy set theory and fuzzy logic are applied in the main in physical and engineering sciences. In a sixteen-page article, modestly titled “Fuzzy Sets” and published in the journal Information and Control, Zadeh launched a multi-disciplinary revolution.

Formally, this collapses to counting in fuzzy sets, interval-valued fuzzy sets or I-fuzzy sets (Atanassov's intuitionistic fuzzy sets).Other applications of intelligent counting in various areas of intelligent systems and decision support will be discussed, too.   The monograph is the first one showing and emphasizing that the presented methods of intelligent counting are human-consistent: are reflections and formalizations of real, human counting procedures performed under imprecision and, possibly, incompleteness of information.