Implementace fuzzy systémů granulární RBF sítí: aplikace pro modelování časových řad

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F07%3A%230000824" target="_blank" >RIV/47813059:19240/07:#0000824 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Granular RBF Neural Network Implementation of Fuzzy Systems: Application to Time Series Modeling

Popis výsledku v původním jazyce

In this study, we are concerned with fuzy systems for mapping input fuzzy sets to output fuzzy sets The RBF (Radial Basic Function) network or other neural network are endowed with some properties that make them more flexible and logically appealing At first, we discuss the basic structure of the fuzzy system as a simple yet powerful fuzzy modeling technique. Neural networks and fuzzy logic models are based on very similar underlying mathematics. The similarity between RBF networks and fuzzy models is noted in detail. Then, we propose the extension of RBF neural networks by the cloud model. Time series approximation and prediction by applying RBF neural networks or fuzzy models and comparisons between the various types of RBF networks and statistical models are discussed at length. An application is included to illustrate the approximation performance of these approaches for economic fuzzy and statistical time series.

Název v anglickém jazyce

Granular RBF Neural Network Implementation of Fuzzy Systems: Application to Time Series Modeling

Popis výsledku anglicky

In this study, we are concerned with fuzy systems for mapping input fuzzy sets to output fuzzy sets The RBF (Radial Basic Function) network or other neural network are endowed with some properties that make them more flexible and logically appealing At first, we discuss the basic structure of the fuzzy system as a simple yet powerful fuzzy modeling technique. Neural networks and fuzzy logic models are based on very similar underlying mathematics. The similarity between RBF networks and fuzzy models is noted in detail. Then, we propose the extension of RBF neural networks by the cloud model. Time series approximation and prediction by applying RBF neural networks or fuzzy models and comparisons between the various types of RBF networks and statistical models are discussed at length. An application is included to illustrate the approximation performance of these approaches for economic fuzzy and statistical time series.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AH - Ekonomie

OECD FORD obor

—

Projekt

<a href="/cs/project/GA402%2F05%2F2768" target="_blank" >GA402/05/2768: Pokročilé statistické a ekonometrické techniky pro modelování a predikci ekonomických časových řad</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2007

Kód důvěrnosti údajů

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

Název periodika

Jourtnalk of Mult.-Valued Logic & Soft Computing

ISSN

1542-3980

e-ISSN

—

Svazek periodika

14

Číslo periodika v rámci svazku

2007

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

401-414

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—