Empirical study on learning in fuzzy systems

Ishibuchi, Hisao; Nozaki, Kengo; Tanaka, Hideo; Hosaka, Yukio; Matsuda, Masanori

doi:10.1109/fuzzy.1993.327419

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…The Takagi-Sugeno-Kang (TSK) model differs from the MM in the IF-THEN rule by employing rules that represent the consequent parts as a function of input variables rather than dealing with linguistic variables (Sugeno and Kang 1988;Takagi and Sugeno 1985). FS.HGD is an FRBS function that utilizes heuristics and the gradient descent approach to implement a simplified TSK fuzzy-rule-generating method proposed by Ishibuchi et al (1993). It is a special case within the TSK model and employs fuzzy rules with non-fuzzy singletons (i.e., real numbers) in the consequent part.…”

Section: Fshgd Modelmentioning

confidence: 99%

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Alenezy

Ismail

Wadi

et al. 2023

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We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the model’s parameters based on the error function gradient, while the HyFIS approach combines the advantages of neural networks and fuzzy logic systems to create a more robust and accurate learning model. The MODWT uses five mathematical functions to form a discrete wavelet basis. The dataset used includes the daily closing prices of the Tadawul stock market from August 2011 to December 2019. Inputs were selected based on multiple regression, tolerance, and variance inflation factor tests, and the oil price (Loil) and repo rate (Repo) were identified as input variables. The output variable is represented by the logarithm of the Tadawul stock market price (LSCS). MODWT-LA8 (ARIMA(1,1,0) with drift) outperforms other WT functions on the 80% dataset, with an ME of (0.00000532), MAE of (0.003214182), and MAPE of (0.06449683). The addition of WT functions to the FS.HGD and HyFIS models increases their forecasting ability. Based on the reduced RMSE (0.048), MAE (0.038), and MAPE (0.538), the MODWT-LA8-FS.HGD outperforms traditional models in predicting the remaining 20% of datasets.

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Section: Fshgd Modelmentioning

confidence: 99%

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Alenezy

Ismail

Wadi

et al. 2023

Risks

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“…The fuzzy control algorithm consists of a set of linguistic rules related by the concepts of fuzzy implication, approximate reasoning, and compositional rule of inference. The linguistic rules are usually derived from expert experience or constructed through an empirical learning process [25]- [27]. Because the detailed dynamics of the controlled process is not needed in the design process, fuzzy control possesses an inherent robustness [28], [29].…”

Section: Fuzzy-tuning Current Control Schemementioning

confidence: 99%

Fuzzy-tuning current-vector control of a three-phase PWM inverter for high-performance AC drives

Tzou

Lin

1998

IEEE Trans. Ind. Electron.

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This paper proposes a new discrete fuzzy-tuning current-vector control (FTC) scheme for three-phase pulsewidth modulation (PWM) inverters. The proposed current control scheme can achieve fast transient responses and, at the same time, have very low total harmonic distortion in output current during steady-state operation. The proposed FTC scheme generates quasi-optimum PWM patterns by using a closed-loop control technique with instantaneous current feedback. The proposed FTC scheme has been realized using a single-chip digital signal processor (TMS320C14) from Texas Instruments. Experimental results are given to verify the proposed fuzzy-tuning current control strategy for three-phase PWM inverters.

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Fuzzy-tuning current-vector control of a 3-phase PWM inverter

Tzou

Proceedings of PESC '95 - Power Electronics Specialist Conference

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Empirical study on learning in fuzzy systems

Cited by 3 publications

References 12 publications

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

Fuzzy-tuning current-vector control of a three-phase PWM inverter for high-performance AC drives

Fuzzy-tuning current-vector control of a 3-phase PWM inverter

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