Fuzzy Cognitive Maps: Basic Theories and Their Application to Complex Systems -- Expert-Based and Computational Methods for Developing Fuzzy Cognitive Maps -- A Novel Approach on Constructed Dynamic Fuzzy Cognitive Maps Using Fuzzified Decision Trees and Knowledge-Extraction Techniques -- The FCM Designer Tool -- Fuzzy Cognitive Networks: Adaptive Network Estimation and Control Paradigms -- Modeling of Operative Risk Using Fuzzy Expert Systems -- Fuzzy Cognitive Maps in Banking Business Process Performance Measurement -- Fuzzy Cognitive Maps-Based IT Projects Risks Scenarios -- Software Reliability Modelling Using Fuzzy Cognitive Maps -- Fuzzy Cognitive Networks for Maximum Power Point Tracking in Photovoltaic Arrays -- Fuzzy Cognitive Maps Applied to Computer Vision Tasks -- Classifying Patterns Using Fuzzy Cognitive Maps -- Dynamic Fuzzy Cognitive Maps for the Supervision of Multiagent Systems -- Soft Computing Technique of Fuzzy Cognitive Maps to Connect Yield Defining Parameters with Yield in Cotton Crop Production in Central Greece as a Basis for a Decision Support System for Precision Agriculture Application -- Analysis of Farmers’ Concepts of Environmental Management Measures: An Application of Cognitive Maps and Cluster Analysis in Pursuit of Modelling Agents’ Behaviour -- Using Fuzzy Cognitive Maps to Support the Analysis of Stakeholders’ Views of Water Resource Use and Water Quality Policy -- Fuzzy Cognitive Map to Support Conflict Analysis in Drought Management.

The theory of cognitive maps was developed in 1976. Its main aim was the representation of (causal) relationships among “concepts” also known as “factors” or “nodes”. Concepts could be assigned values. Causal relationships between two concepts could be of three types: positive, negative or neutral. Increase in the value of a concept would yield a corresponding positive or negative increase at the concepts connected to it via relationships. In 1986 Bart Kosko introduced the notion of fuzziness to cognitive maps and created the theory of Fuzzy Cognitive Maps (FCMs). The relationship between two concepts in (FCMs) can take a value in the interval [-1,1]. This relationship value is called “weight”. For the last twenty years extensive research in the theory of FCMs has been performed that provided major improvements and enhancements in its theoretical underpinning. New methodologies and approaches have been developed. FCMs have also been applied to many different sectors. New software tools have been developed that automate FCM creation and management. The aim of this book is to present recent advances and state of the art in FCM theory, methodologies, applications and tools that exist to date scattered in journal papers, in a concrete and integrated manner.