LNAI 2903 Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule 1st Edition by Elpiniki Papageorgiou, Chrysostomos Stylios, Peter Groumpos ISBN 9783540206460 354020646X

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Product details:
ISBN 10: 354020646X
ISBN 13: 9783540206460
Author: Elpiniki Papageorgiou, Chrysostomos Stylios, Peter Groumpos

Fuzzy Cognitive Map (FCM) is a soft computing technique for modeling systems. It combines synergistically the theories of neural networks and fuzzy logic. The methodology of developing FCMs is easily adaptable but relies on human experience and knowledge, and thus FCMs exhibit weaknesses and dependence on human experts. The critical dependence on the expert’s opinion and knowledge, and the potential convergence to undesired steady states are deficiencies of FCMs. In order to overcome these deficiencies and improve the efficiency and robustness of FCM a possible solution is the utilization of learning methods. This research work proposes the utilization of the unsupervised Hebbian algorithm to nonlinear units for training FCMs. Using the proposed learning procedure, the FCM modifies its fuzzy causal web as causal patterns change and as experts update their causal knowledge.

LNAI 2903 Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule 1st Edition Table of contents:

Chapter 1: Introduction to Fuzzy Cognitive Maps (FCM)

• 1.1. Definition and Concepts of Fuzzy Cognitive Maps
• 1.2. The History and Evolution of FCMs
• 1.3. Fuzzy Cognitive Maps in Decision Making and Modeling
• 1.4. Real-World Applications of Fuzzy Cognitive Maps
• 1.5. Objectives of the Book and Motivation for Research

Chapter 2: Basic Principles of Cognitive Maps

• 2.1. Cognitive Maps and Their Use in System Modeling
• 2.2. Components of Fuzzy Cognitive Maps: Concepts, Links, and Weights
• 2.3. Structure and Dynamics of Fuzzy Cognitive Maps
• 2.4. Types of Cognitive Maps: Traditional vs. Fuzzy Cognitive Maps
• 2.5. Analysis and Interpretation of FCM Behavior

Chapter 3: Hebbian Learning: A Theoretical Overview

• 3.1. Introduction to Hebbian Learning
• 3.2. The Classical Hebbian Rule
• 3.3. The Biological Basis of Hebbian Learning
• 3.4. Applications and Extensions of Hebbian Learning
• 3.5. Nonlinear Extensions to Hebbian Learning

Chapter 4: Nonlinear Hebbian Learning Rules

• 4.1. Introduction to Nonlinear Hebbian Learning
• 4.2. The Role of Nonlinearities in Neural Learning
• 4.3. Types of Nonlinear Hebbian Learning Rules
• 4.4. Nonlinear Hebbian Learning in FCMs
• 4.5. Mathematical Foundations of Nonlinear Hebbian Learning

Chapter 5: Fuzzy Cognitive Maps and Learning Algorithms

• 5.1. FCMs as a Knowledge Representation Tool
• 5.2. Traditional Learning Algorithms for FCMs
• 5.3. The Role of Learning in Fuzzy Cognitive Maps
• 5.4. Integration of Learning in FCMs: Supervised vs. Unsupervised
• 5.5. Challenges in Learning and Model Adaptation

Chapter 6: Fuzzy Cognitive Map Learning Based on Nonlinear Hebbian Rule

• 6.1. Introduction to the Proposed Learning Framework
• 6.2. Nonlinear Hebbian Rule in the Context of FCMs
• 6.3. Mathematical Formulation of the Learning Rule
• 6.4. The Dynamics of the Learning Process in FCMs
• 6.5. Convergence and Stability of the Learning Process
• 6.6. Practical Implementation Considerations

Chapter 7: Experimental Setup and Simulation Studies

• 7.1. Experimental Design for Evaluating FCM Learning
• 7.2. Selection of Datasets and Case Studies
• 7.3. Simulation Tools and Software Implementations
• 7.4. Evaluation Metrics for FCM Performance
• 7.5. Case Study 1: Control Systems Modeling
• 7.6. Case Study 2: Environmental Modeling
• 7.7. Case Study 3: Decision Support Systems

Chapter 8: Application of Nonlinear Hebbian-Based FCMs in Real-World Problems

• 8.1. Applications in System Control
• 8.2. FCMs in Dynamic System Simulation and Forecasting
• 8.3. Decision Support Systems for Complex Environments
• 8.4. Cognitive Modeling in Social and Psychological Systems
• 8.5. Challenges and Limitations in Real-World Applications

Chapter 9: Advanced Topics and Extensions

• 9.1. Hybrid Approaches: Combining FCMs with Other Learning Methods
• 9.2. Multi-Layer and Multi-Agent Fuzzy Cognitive Maps
• 9.3. FCMs for Knowledge Discovery and Data Mining
• 9.4. Fuzzy Cognitive Maps in Neural Networks and Deep Learning
• 9.5. Enhancing the Robustness and Scalability of FCMs

Chapter 10: Conclusion and Future Research Directions

• 10.1. Summary of Key Findings
• 10.2. Future Directions in Nonlinear Hebbian Learning
• 10.3. Potential Areas for Further Research in Fuzzy Cognitive Maps
• 10.4. Concluding Remarks

References

• Comprehensive listing of books, journal articles, conference papers, and other scholarly works cited throughout the book.

Index

• Alphabetical listing of terms, concepts, and algorithms for easy reference.

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