Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training

Document Type : Original Article

Authors

1 Educational Psychology, Grand Valley State University, Grand Rapids, MI 49504, United States

2 State Grid Shandong Electric Power Research Institute, China

Abstract

Predicting time series, especially those originating from chaotic and nonlinear dynamic systems, is a critical research area with broad applications across various fields. Neural networks and fuzzy systems have emerged as leading methods for forecasting chaotic time series. This study introduces an improved adaptive neural-fuzzy inference system (ANFIS) specifically tailored for forecasting chaotic time series. Unlike traditional ANFIS models, which are primarily designed for static problems, this enhanced version incorporates self-feedback relationships from previous outputs to capture the time dependencies inherent in dynamic systems. Additionally, a hybrid approach combining the Imperialist Competitive Optimization Algorithm (ICA) and Least Squares Estimation (LSE) is employed to train the neural-fuzzy system and update its parameters. This method circumvents challenges associated with training gradient-based algorithms. The proposed technique is applied to predict and model multiple nonlinear and chaotic time series from real-world scenarios. Comparative analyses with recent works demonstrate the superior performance of the proposed method, particularly in terms of the prediction total error criterion for time series modeling and forecasting. These results highlight the effectiveness of incorporating self-feedback relationships and utilizing the CCA-LSE hybrid approach in enhancing the predictive capabilities of adaptive neural-fuzzy inference systems for chaotic time series.

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History

  • Receive Date: 27 February 2024
  • Revise Date: 28 March 2024
  • Accept Date: 30 March 2024

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