An adaptive recurrent fuzzy system for nonlinear identification

Savran, Aydoğan

doi:10.1016/j.asoc.2006.02.010

Cited by 17 publications

(18 citation statements)

References 30 publications

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“…Fuzzy logic systems are universal approximations that can uniformly estimate nonlinear continuous functions with arbitrary accuracy [13,14]. The fuzzy model is a piecewise interpolation of several models that operates using membership functions.…”

Section: The Fuzzy Garch Modelmentioning

confidence: 99%

“…For the functional fuzzy system, we can use an appropriate operation for representing the premise (e.g., the minimum) [13], and defuzzification may be implemented as follows.…”

Section: The Fuzzy Garch Modelmentioning

confidence: 99%

“…In this paper, we propose a fuzzy GARCH model based on fuzzy systems [13][14][15][16][17]. Fuzzy modeling methods are promising techniques for describing complex dynamics and asymmetries in systems.…”

Section: Introductionmentioning

confidence: 99%

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Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility

Hung¹

2011

Applied Soft Computing

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Section: The Fuzzy Garch Modelmentioning

confidence: 99%

“…For the functional fuzzy system, we can use an appropriate operation for representing the premise (e.g., the minimum) [13], and defuzzification may be implemented as follows.…”

Section: The Fuzzy Garch Modelmentioning

confidence: 99%

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Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility

Hung¹

2011

Applied Soft Computing

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“…In Anderson and Miller [2], it is emphasized that the controller design for the bioreactor benchmark problem is a challenge due to the nonlinearity and a set of complicated regimes are caused by the nonlinear interactions in between the variables involved. Recent works focusing on the bioreactor benchmark problem consider the adaptive learning based identification in Zou et al [39], ANFIS based identification in Savran [26] and hybrid control methods combining fuzzy systems and SVMs a in Serdar [15]. Sliding mode control based approaches are also tested on the bioreactor model, such as Efe [7] considers a multi-input multi-output version of the problem whereas Tokat [30] elaborates the switching line adaptation to meet the closed loop performance expectations.…”

Section: Introductionmentioning

confidence: 99%

A comparison of networked approximators in parallel mode identification of a bioreactor

Efe

2010

Advances in Engineering Software

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“…Due to these shortcomings, models based on fuzzy theory appear as an alternative methodology to evaluate high nonlinear systems (Zadeh, 2005, Savran, 2007. Popov & Bykhanov (2005) combined the concept of fuzzy rules and GARCH approach to model volatility of financial time series.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting

Maciel¹

2013

Advances in Financial Risk Management

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Forecasting stock market returns volatility is a challenging task that has attracted the attention of market practitioners, regulators and academics in recent years. This paper proposes a Fuzzy GJR-GARCH model to forecast the volatility of S&P 500 and Ibovespa indexes. The model comprises both the concept of fuzzy inference systems and GJR-GARCH modeling approach in order to consider the principles of time-varying volatility, leverage effects and volatility clustering, in which changes are cataloged by similarity. Moreover, a differential evolution (DE) algorithm is suggested to solve the problem of Fuzzy GJR-GARCH parameters estimation. The results indicate that the proposed method offers significant improvements in volatility forecasting performance in comparison with GARCH-type models and with a current Fuzzy-GARCH model reported in the literature. Furthermore, the DEbased algorithm aims to achieve an optimal solution with a rapid convergence rate.Keywords: volatility; GARCH models; fuzzy systems; differential evolution. JEL codes: C53; C61; G17. ResumoA previsão da volatilidade dos retornos de ativos financeirosé uma abordagem desafiadora e tem atraído a atenção de participantes do mercado, reguladores e acadêmicos nos anos recentes. Este artigo propõe um modelo GJR-GARCH nebuloso para a previsão da volatilidade dosíndices S&P 500 e Ibovespa. O modelo combina os conceitos de sistemas de inferência nebulosos e a abordagem GJR-GARCH para considerar os príncipios de variância condicional, efeitos de alavancagem e agrupamentos de volatilidade, em que as flutuações são associadas por similaridade. Além disso, um algoritmo de evolução diferencial (ED)é sugerido para solucionar o problema de estimação dos parâmetros do modelo GJR-GARCH nebuloso. Os resultados indicaram que o método proposto permitiu uma melhor capacidade de previsão da volatilidade em comparação com modelos da família GARCH e com um modelo GARCH nebuloso recentemente proposto na literatura. Além disso, o algoritmo de ED considerado obteve soluçõesótimas com uma rápida taxa de convergência.

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An adaptive recurrent fuzzy system for nonlinear identification

Cited by 17 publications

References 30 publications

Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility

Applying a combined fuzzy systems and GARCH model to adaptively forecast stock market volatility

A comparison of networked approximators in parallel mode identification of a bioreactor

A Hybrid Fuzzy GJR-GARCH Modeling Approach for Stock Market Volatility Forecasting

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