scholarly journals Dynamics of Fuzzy-Rough Cognitive Networks

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 881
Author(s):  
István Á. Harmati
Keyword(s):  
Cognitive Maps ◽  
Black Box ◽  
Discrete Dynamical Systems ◽  
Cognitive Networks ◽  
Fuzzy Cognitive Maps ◽  
Classification Problems ◽  
Mathematical Properties ◽  
Structural Differences ◽  
Theoretical Issues

Fuzzy-rough cognitive networks (FRCNs) are interpretable recurrent neural networks, primarily designed for solving classification problems. Their structure is simple and transparent, while the performance is comparable to the well-known black-box classifiers. Although there are many applications on fuzzy cognitive maps and recently for FRCNS, only a very limited number of studies discuss the theoretical issues of these models. In this paper, we examine the behaviour of FRCNs viewing them as discrete dynamical systems. It will be shown that their mathematical properties highly depend on the size of the network, i.e., there are structural differences between the long-term behaviour of FRCN models of different size, which may influence the performance of these modelling tools.

Application of evolutionary fuzzy cognitive maps to the long-term prediction of prostate cancer

2012 ◽  
Vol 12 (12) ◽  
pp. 3810-3817 ◽  
Author(s):  
Wojciech Froelich ◽  
Elpiniki I. Papageorgiou ◽  
Michael Samarinas ◽  
Konstantinos Skriapas
Keyword(s):  
Prostate Cancer ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Term Prediction ◽  
Long Term Prediction

scholarly journals Fuzzy Cognitive Maps based Mathematical Model for Prediction of “Parkinson Disease”

2020 ◽  
Vol 8 (6) ◽  
pp. 1146-1149
Keyword(s):  
Mathematical Model ◽  
Central Nervous System ◽  
Nervous System ◽  
Parkinson Disease ◽  
Early Stage ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
The Central Nervous System ◽  
Diagnostic Symptoms

This note explains about “Parkinson Disease which may be a long-term disorder of the central nervous system”. The research paper focuses on analysis of symptoms of “Parkinson Disease” to predict the disease in early stage. Concept of FCMs was used to interpret the diagnostic symptoms of “Parkinson Disease”. The target is to draw connection between the symptoms and provide likely explanation.

Analysis of a new business model to fundraise non-governmental organizations using fuzzy cognitive maps

2020 ◽  
Vol 39 (5) ◽  
pp. 6231-6243
Author(s):  
Irem Ucal Sari ◽  
Duygu Sergi ◽  
Can Aytore
Keyword(s):  
Search Engine ◽  
Learning Algorithm ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Sigmoid Function ◽  
Long Term Effects ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
Critical Issues

Fundraising is one of the most critical issues for non-governmental organizations (NGOs) to carry out their projects. In this paper, a search engine project which aims to find additional financial sources and increase donations for NGOs is proposed. The proposed search engine project is analyzed using fuzzy cognitive maps (FCMs) to define and manage factor influences on the success of the project. FCMs are useful tools to define long term effects of important factors for a system. First casual relations of the factors are determined and then using sigmoid function for learning algorithm, the equilibrium state for the system is obtained. It is found that the factors generating monetary values are the most important ones for the project to be successful in long term.

Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems

2012 ◽  
Vol 39 (12) ◽  
pp. 10620-10629 ◽  
Author(s):  
G.A. Papakostas ◽  
D.E. Koulouriotis ◽  
A.S. Polydoros ◽  
V.D. Tourassis
Keyword(s):  
Pattern Classification ◽  
Hebbian Learning ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Classification Problems

FUZZY COGNITIVE MAPS FOR PATTERN RECOGNITION APPLICATIONS

2008 ◽  
Vol 22 (08) ◽  
pp. 1461-1486 ◽  
Author(s):  
G. A. PAPAKOSTAS ◽  
Y. S. BOUTALIS ◽  
D. E. KOULOURIOTIS ◽  
B. G. MERTZIOS
Keyword(s):  
Pattern Recognition ◽  
Pattern Classification ◽  
Degrees Of Freedom ◽  
Cognitive Maps ◽  
Classification Problem ◽  
Research Field ◽  
Fuzzy Cognitive Maps ◽  
Classification Problems ◽  
Hybrid Classifier ◽  
Classification Structure

A first attempt to incorporate Fuzzy Cognitive Maps (FCMs), in pattern classification applications is performed in this paper. Fuzzy Cognitive Maps, as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the behavior of any system. By transforming a pattern classification problem into a problem of discovering the way the sets of patterns interact with each other and with the classes that they belong to, we could describe the problem in terms of Fuzzy Cognitive Maps. More precisely, some FCM architectures are introduced and studied with respect to their pattern recognition abilities. An efficient novel hybrid classifier is proposed as an alternative classification structure, which exploits both neural networks and FCMs to ensure improved classification capabilities. Appropriate experiments with four well-known benchmark classification problems and a typical computer vision application establish the usefulness of the Fuzzy Cognitive Maps, in a pattern recognition research field. Moreover, the present paper introduces the use of more flexible FCMs by incorporating nodes with adaptively adjusted activation functions. This advanced feature gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.

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