Interactive evolutionary optimization of fuzzy cognitive maps

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50005185" target="_blank" >RIV/62690094:18450/17:50005185 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0925231216315636" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231216315636</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neucom.2016.10.068" target="_blank" >10.1016/j.neucom.2016.10.068</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interactive evolutionary optimization of fuzzy cognitive maps

Popis výsledku v původním jazyce

Modeling dynamic systems with Fuzzy Cognitive Maps (FCMs) is characterized by the simplicity of the model representation and its execution. Furthermore, FCMs can easily incorporate human knowledge from the given domain. Despite the many advantages of FCMs, there are some drawbacks, too. The quality of knowledge obtained from the domain experts, and any differences and uncertainties in their opinions, has to be improved by different methods. We propose a new approach for handling incompleteness and natural uncertainty in expert evaluation of the connection matrix of a particular FCM. It is based on partial expert estimations and evolutionary algorithms in the role of an expert-driven optimization and outside of the FCM optimization (adaptation) research area known as Interactive Evolutionary Computing (IEC). In the present paper, a modification of IEC for the purposes of FCM optimization is presented, referred to as the IEO-FCM method, i.e., the Interactive Evolutionary Optimization of Fuzzy Cognitive Maps. Experimental results on two control problems suggest that the IEO-FCM method can improve the quality of an FCM even in situations without any measured data necessary for other known learning algorithms.

Název v anglickém jazyce

Interactive evolutionary optimization of fuzzy cognitive maps

Popis výsledku anglicky

Modeling dynamic systems with Fuzzy Cognitive Maps (FCMs) is characterized by the simplicity of the model representation and its execution. Furthermore, FCMs can easily incorporate human knowledge from the given domain. Despite the many advantages of FCMs, there are some drawbacks, too. The quality of knowledge obtained from the domain experts, and any differences and uncertainties in their opinions, has to be improved by different methods. We propose a new approach for handling incompleteness and natural uncertainty in expert evaluation of the connection matrix of a particular FCM. It is based on partial expert estimations and evolutionary algorithms in the role of an expert-driven optimization and outside of the FCM optimization (adaptation) research area known as Interactive Evolutionary Computing (IEC). In the present paper, a modification of IEC for the purposes of FCM optimization is presented, referred to as the IEO-FCM method, i.e., the Interactive Evolutionary Optimization of Fuzzy Cognitive Maps. Experimental results on two control problems suggest that the IEO-FCM method can improve the quality of an FCM even in situations without any measured data necessary for other known learning algorithms.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neurocomputing

ISSN

0925-2312

e-ISSN

—

Svazek periodika

232

Číslo periodika v rámci svazku

April 2017

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

58-68

Kód UT WoS článku

000393532800006

EID výsledku v databázi Scopus

2-s2.0-85009286617