Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training
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...
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Bilijipub publisher
2024-03-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| Online Access: | https://aeis.bilijipub.com/article_193340_437c6793ff88ade541017f5c39384838.pdf |
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| author | Andrew Topper Honglei Yao |
| author_facet | Andrew Topper Honglei Yao |
| author_sort | Andrew Topper |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5fde5e0df80c4df898028911f583d94c |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-5fde5e0df80c4df898028911f583d94c2025-02-12T08:47:46ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632024-03-01003019911410.22034/aeis.2024.445975.1175193340Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid TrainingAndrew Topper0Honglei Yao1Grand Valley State University, Grand Rapids, Michigan, 49504, United StatesState Grid Shandong Electric Power Research Institute, Shandong, ChinaPredicting 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.https://aeis.bilijipub.com/article_193340_437c6793ff88ade541017f5c39384838.pdfevolutionary algorithmslearning algorithmleast squares errorchaotic systems |
| spellingShingle | Andrew Topper Honglei Yao Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training Advances in Engineering and Intelligence Systems evolutionary algorithms learning algorithm least squares error chaotic systems |
| title | Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training |
| title_full | Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training |
| title_fullStr | Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training |
| title_full_unstemmed | Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training |
| title_short | Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training |
| title_sort | enhanced adaptive neural fuzzy inference system for dynamic time series prediction using self feedback and hybrid training |
| topic | evolutionary algorithms learning algorithm least squares error chaotic systems |
| url | https://aeis.bilijipub.com/article_193340_437c6793ff88ade541017f5c39384838.pdf |
| work_keys_str_mv | AT andrewtopper enhancedadaptiveneuralfuzzyinferencesystemfordynamictimeseriespredictionusingselffeedbackandhybridtraining AT hongleiyao enhancedadaptiveneuralfuzzyinferencesystemfordynamictimeseriespredictionusingselffeedbackandhybridtraining |