Fuzzy Logic In Data Modeling
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Fuzzy Logic in Data Modeling
Author: Guoqing Chen
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
also in: THE KLUWER INTERNATIONAL SERIES ON ASIAN STUDIES IN COMPUTER AND INFORMATION SCIENCE, Volume 2
Advanced Fuzzy Logic Technologies in Industrial Applications
Author: Ying Bai
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-01-17
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. In the mid-1960s and contemporary with Kalman’s pioneering papers on sta- space models and optimal control, L.A. Zadeh began publishing papers on “fuzzy sets”. It took another decade before the fuzzy-logic controller due to Mamdani and Assilion was reported in the literature (ca. 1974), and now the fuzzy-logic control paradigm is entering its fifth decade of development and application. Thus, this new Advances in Industrial Control monograph edited by Ying Bai, Hanqi Zhuang and Dali Wang on fuzzy-logic control and its practical application comes as a timely reminder of the wide range of problems that can be solved by this continually evolving methodology.
Fuzzy Logic for Business, Finance, and Management
This is truly an interdisciplinary book for knowledge workers in business, finance, management and socio-economic sciences based on fuzzy logic. It serves as a guide to and techniques for forecasting, decision making and evaluations in an environment involving uncertainty, vagueness, impression and subjectivity. Traditional modeling techniques, contrary to fuzzy logic, do not capture the nature of complex systems especially when humans are involved. Fuzzy logic uses human experience and judgement to facilitate plausible reasoning in order to reach a conclusion. Emphasis is on applications presented in the 27 case studies including Time Forecasting for Project Management, New Product Pricing, and Control of a Parasit-Pest System.