2,521 research outputs found
Simulation of complex environments:the Fuzzy Cognitive Agent
The world is becoming increasingly competitive by the action of liberalised national and global markets. In parallel these markets have become increasingly complex making it difficult for participants to optimise their trading actions. In response, many differing computer simulation techniques have been investigated to develop either a deeper understanding of these evolving markets or to create effective system support tools. In this paper we report our efforts to develop a novel simulation platform using fuzzy cognitive agents (FCA). Our approach encapsulates fuzzy cognitive maps (FCM) generated on the Matlab Simulink platform within commercially available agent software. We firstly present our implementation of Matlab Simulink FCMs and then show how such FCMs can be integrated within a conceptual FCA architecture. Finally we report on our efforts to realise an FCA by the integration of a Matlab Simulink based FCM with the Jack Intelligent Agent Toolkit
Super Fuzzy Matrices and Super Fuzzy Models for Social Scientists
This book introduces the concept of fuzzy super matrices and operations on
them. This book will be highly useful to social scientists who wish to work
with multi-expert models. Super fuzzy models using Fuzzy Cognitive Maps, Fuzzy
Relational Maps, Bidirectional Associative Memories and Fuzzy Associative
Memories are defined here. The authors introduce 13 multi-expert models using
the notion of fuzzy supermatrices. These models are described with illustrative
examples. This book has three chapters. In the first chaper, the basic concepts
about super matrices and fuzzy super matrices are recalled. Chapter two
introduces the notion of fuzzy super matrices adn their properties. The final
chapter introduces many super fuzzy multi expert models.Comment: 280 page
Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps
As extension of Fuzzy Cognitive Maps are now introduced the Neutrosophic Cognitive Map
SciTech News Volume 71, No. 2 (2017)
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Basic Neutrosophic Algebraic Structures and their Application to Fuzzy and Neutrosophic Models
The involvement of uncertainty of varying degrees when the total of the
membership degree exceeds one or less than one, then the newer mathematical
paradigm shift, Fuzzy Theory proves appropriate. For the past two or more
decades, Fuzzy Theory has become the potent tool to study and analyze
uncertainty involved in all problems. But, many real-world problems also abound
with the concept of indeterminacy. In this book, the new, powerful tool of
neutrosophy that deals with indeterminacy is utilized. Innovative neutrosophic
models are described. The theory of neutrosophic graphs is introduced and
applied to fuzzy and neutrosophic models. This book is organized into four
chapters. In Chapter One we introduce some of the basic neutrosophic algebraic
structures essential for the further development of the other chapters. Chapter
Two recalls basic graph theory definitions and results which has interested us
and for which we give the neutrosophic analogues. In this chapter we give the
application of graphs in fuzzy models. An entire section is devoted for this
purpose. Chapter Three introduces many new neutrosophic concepts in graphs and
applies it to the case of neutrosophic cognitive maps and neutrosophic
relational maps. The last section of this chapter clearly illustrates how the
neutrosophic graphs are utilized in the neutrosophic models. The final chapter
gives some problems about neutrosophic graphs which will make one understand
this new subject.Comment: 149 pages, 130 figure
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