Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

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 5

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

52-57

Kód UT WoS článku

000393532800005

EID výsledku v databázi Scopus

2-s2.0-85008502691