Fuzzy Cognitive Maps for Applied Sciences and Engineering (Record no. 93356)

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001 - CONTROL NUMBER
control field 978-3-642-39739-4
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220082519.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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fixed length control field 131202s2014 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783642397394
-- 978-3-642-39739-4
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-642-39739-4
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q342
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM004000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Papageorgiou, Elpiniki I.
Relator term editor.
245 10 - TITLE STATEMENT
Title Fuzzy Cognitive Maps for Applied Sciences and Engineering
Medium [electronic resource] :
Remainder of title From Fundamentals to Extensions and Learning Algorithms /
Statement of responsibility, etc edited by Elpiniki I. Papageorgiou.
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg :
-- Imprint: Springer,
-- 2014.
300 ## - PHYSICAL DESCRIPTION
Extent XXVII, 395 p. 147 illus., 2 illus. in color.
Other physical details online resource.
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-- computer
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-- online resource
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347 ## -
-- text file
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490 1# - SERIES STATEMENT
Series statement Intelligent Systems Reference Library,
International Standard Serial Number 1868-4394 ;
Volume number/sequential designation 54
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Methods and Algorithms for Fuzzy Cognitive Map-based Modeling -- Fuzzy Cognitive Maps as representations of mental models and group beliefs -- FCM Relationship Modeling for Engineering Systems -- Using RuleML for Representing and Prolog for Simulating Fuzzy Cognitive Maps -- Fuzzy Web Knowledge Aggregation, Representation, and Reasoning for Online Privacy and Reputation Management -- Decision Making by Rule-Based Fuzzy Cognitive Maps: An Approach to Implement Student-Centered Education -- Extended Evolutionary Learning of Fuzzy Cognitive Maps for the Prediction of Multivariate Time-Series -- Synthesis and Analysis of Multi-Step Algorithms of Fuzzy Cognitive Maps Learning -- Designing and Training Relational Fuzzy Cognitive Maps -- Cooperative Autonomous Agents Based On Dynamical Fuzzy Cognitive Maps -- FCM-GUI: A graphical user interface for Big Bang-Big -- Crunch Learning for FCM and Evaluation -- JFCM - A Java library for Fuzzy Cognitive Maps -- Use and evaluation of FCM as a tool for long term socio ecological research -- Application of Fuzzy Grey Cognitive Maps for process problems in industry Papageorgiou -- Use and Perspectives of Fuzzy Cognitive Maps in Robotics -- Fuzzy Cognitive Maps for Structural Damage Detection -- Fuzzy cognitive strategic maps for business management -- The Complex Nature of Migration at a Conceptual Level -- Overlook to the Internal Migration Experience in Gebze through Fuzzy Cognitive Mapping Method -- Understanding Public Participation and Combining Perceptions of Stakeholders’ for a Better Management in Danube Delta Biosphere Reserve -- Employing Fuzzy Cognitive Map for Periodontal Disease Assessment.
520 ## - SUMMARY, ETC.
Summary, etc Fuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts” bearing different states of activation depending on the knowledge they represent, and the “edges” denoting the causal effects that each source node exercises on the receiving concept expressed through weights. Weights take values in the interval [-1,1], which denotes the positive, negative or neutral causal relationship between two concepts. An FCM can be typically obtained through linguistic terms, inherent to fuzzy systems, but with a structure similar to the neural networks, which facilitates data processing, and has capabilities for training and adaptation. During the last 10 years, an exponential growth of published papers in FCMs was followed showing great impact potential. Different FCM structures and learning schemes have been developed, while numerous studies report their use in many contexts with highly successful modeling results.   The aim of this book is to fill the existing gap in the literature concerning fundamentals, models, extensions and learning algorithms for FCMs in knowledge engineering. It comprehensively covers the state-of-the-art FCM modeling and learning methods, with algorithms, codes and software tools, and provides a set of applications that demonstrate their various usages in applied sciences and engineering.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783642397387
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Intelligent Systems Reference Library,
-- 1868-4394 ;
Volume number/sequential designation 54
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-39739-4
912 ## -
-- ZDB-2-ENG

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