Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50013459" target="_blank" >RIV/62690094:18450/17:50013459 - isvavai.cz</a>
Result on the web
<a href="http://www.sciencedirect.com/science/article/pii/S0925231216315661" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0925231216315661</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neucom.2016.10.070" target="_blank" >10.1016/j.neucom.2016.10.070</a>
Alternative languages
Result language
angličtina
Original language name
Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes
Original language description
In this paper, we propose a Fuzzy Cognitive Map (FCM) learning approach with a multi-local search in balanced memetic algorithms for forecasting industrial drying processes. The first contribution of this paper is to propose a FCM model by an Evolutionary Algorithm (EA), but the resulted FCM model is improved by a multi-local and balanced local search algorithm. Memetic algorithms can be tuned with different local search strategies (CMA-ES, SW, SSW and Simplex) and the balance of the effort between global and local search. To do this, we applied the proposed approach to the forecasting of moisture loss in industrial drying process. The thermal drying process is a relevant one used in many industrial processes such as food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries, and others. This research also shows that exploration of the search space is more relevant than finding local optima in the FCM models tested.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neurocomputing
ISSN
0925-2312
e-ISSN
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Volume of the periodical
232
Issue of the periodical within the volume
April 5
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
6
Pages from-to
52-57
UT code for WoS article
000393532800005
EID of the result in the Scopus database
2-s2.0-85008502691