Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms

✍ Scribed by Elpiniki I. Papageorgiou, Jose L. Salmeron (auth.), Elpiniki I. Papageorgiou (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2014
• Tongue: English
• Leaves: 411
• Series: Intelligent Systems Reference Library 54
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxvii
Methods and Algorithms for Fuzzy Cognitive Map-based Modeling....Pages 1-28
Fuzzy Cognitive Maps as Representations of Mental Models and Group Beliefs....Pages 29-48
FCM Relationship Modeling for Engineering Systems....Pages 49-64
Using RuleML for Representing and Prolog for Simulating Fuzzy Cognitive Maps....Pages 65-87
Fuzzy Web Knowledge Aggregation, Representation, and Reasoning for Online Privacy and Reputation Management....Pages 89-105
Decision Making by Rule-Based Fuzzy Cognitive Maps: An Approach to Implement Student-Centered Education....Pages 107-120
Extended Evolutionary Learning of Fuzzy Cognitive Maps for the Prediction of Multivariate Time-Series....Pages 121-131
Synthesis and Analysis of Multi-Step Learning Algorithms for Fuzzy Cognitive Maps....Pages 133-144
Designing and Training Relational Fuzzy Cognitive Maps....Pages 145-157
Cooperative Autonomous Agents Based on Dynamical Fuzzy Cognitive Maps....Pages 159-175
FCM-GUI: A Graphical User Interface for Big Bang-Big Crunch Learning of FCM....Pages 177-198
JFCM : A Java Library for FuzzyCognitive Maps....Pages 199-220
Use and Evaluation of FCM as a Tool for Long Term Socio Ecological Research....Pages 221-236
Using Fuzzy Grey Cognitive Maps for Industrial Processes Control....Pages 237-252
Use and Perspectives of Fuzzy Cognitive Maps in Robotics....Pages 253-266
Fuzzy Cognitive Maps for Structural Damage Detection....Pages 267-290
Fuzzy Cognitive Strategic Maps....Pages 291-318
The Complex Nature of Migration at a Conceptual Level: An Overlook of the Internal Migration Experience of Gebze Through Fuzzy Cognitive Mapping Method....Pages 319-354
Understanding Public Participation and Perceptions of Stakeholders for a Better Management in Danube Delta Biosphere Reserve (Romania)....Pages 355-374
Employing Fuzzy Cognitive Map for Periodontal Disease Assessment....Pages 375-389
Back Matter....Pages 391-395

✦ Subjects

Computational Intelligence; Artificial Intelligence (incl. Robotics)