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Related Concept Videos

Modeling with Differential Equations01:25

Modeling with Differential Equations

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Related Experiment Videos

A Sugeno-multiplicative neuron hybrid system trained by differential evolution algorithm for time series forecasting.

Erol Egrioglu1,2,3, Eren Bas1,2,3, Asiye Nur Yıldırım4,5,6

  • 1Department of Data Science and Analytics, Faculty of Arts and Science, Giresun University, 28200, Giresun, Turkey.

Scientific Reports
|May 8, 2026
PubMed
Summary

This study introduces a hybrid neuro-fuzzy model enhancing Sugeno systems with nonlinear multiplicative neurons for improved financial time series forecasting. The novel approach offers superior accuracy and stability, especially in volatile markets.

Keywords:
Artificial neural networksDifferential EvolutionForecastingHybrid fuzzy–neural modelMultiplicative neuronSugeno fuzzy system

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computational Finance
  • Time Series Analysis

Background:

  • Traditional Sugeno fuzzy inference systems often use linear consequents, limiting their ability to model complex nonlinear dynamics.
  • Neural networks offer strong nonlinear modeling but can lack interpretability.
  • Financial time series data exhibit complex, nonlinear, and volatile patterns requiring advanced forecasting techniques.

Purpose of the Study:

  • To propose a novel hybrid neuro-fuzzy forecasting framework integrating Sugeno interpretability with multiplicative neuron nonlinearity.
  • To enhance the representation power of Sugeno models by replacing linear consequents with nonlinear multiplicative neuron structures.
  • To evaluate the proposed model's performance on real-world financial time series data.

Main Methods:

  • Developed a hybrid neuro-fuzzy model combining Sugeno fuzzy inference with multiplicative neuron models.
  • Employed Differential Evolution for optimizing nonlinear parameters and avoiding local minima during training.
  • Evaluated the model on ERussell and ETH/USD financial time series datasets for 10, 20, and 30 steps ahead forecasting.

Main Results:

  • The proposed hybrid model demonstrated superior forecasting accuracy and stability compared to classical Sugeno systems and other neural network approaches.
  • Performance was validated using root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics.
  • The model excelled particularly in forecasting nonlinear and volatile financial time series conditions.

Conclusions:

  • Structurally enhancing Sugeno consequents with nonlinear multiplicative neurons significantly improves time series forecasting capabilities.
  • The hybrid neuro-fuzzy framework offers a powerful and interpretable solution for complex financial forecasting tasks.
  • This approach advances the application of fuzzy systems in computational finance and machine learning.