Fuzzy Neural Network-Based Iterative Multi-Step Prediction with Time Delay for WWTP

Multi-step prediction is developed to exploit the current values of variables to estimate the future behavior of WWTP over multiple timesteps. However, since the existence of time delay can influence the data distribution and the relationship between variables in WWTP, the performance of the multi-s...

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Bibliographic Details
Published in:Chinese Control Conference pp. 1317 - 1322
Main Authors: Xu, Yumeng, Liu, Zheng, Han, Honggui, Sun, Haoyuan
Format: Conference Proceeding
Language:English
Published: Technical Committee on Control Theory, Chinese Association of Automation 28.07.2025
Subjects:
Accuracy
Adaptation models
adaptive second-order algorithm
Adaptive systems
Delay effects
dynamic time delay mechanism
Fuzzy control
Fuzzy neural network
Fuzzy neural networks
Heuristic algorithms
Iterative algorithms
iterative multi-step strategy
Prediction algorithms
Predictive models
ISSN:1934-1768
Online Access:Get full text
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Summary:Multi-step prediction is developed to exploit the current values of variables to estimate the future behavior of WWTP over multiple timesteps. However, since the existence of time delay can influence the data distribution and the relationship between variables in WWTP, the performance of the multi-step prediction model will be degraded. To address this issue, a fuzzy neural network-based iterative multi-step prediction (FNN-IMSP) with time delay for WWTP is proposed in this paper. First, a dynamic time delay mechanism is designed to determine the appropriate time delay at each time point among process variables in WWTP. Then, the proposed mechanism can reveal the dynamical correlation between variables to assist in the multi-step prediction. Second, an iterative multi-step strategy, leveraging both previous and current values, is introduced in the FNN-based prediction method. Then, the proposed prediction model can estimate further future variation tendency. Third, an adaptive second-order algorithm is employed to update the parameters of FNN-IMSP. Then, the adaptive ability and prediction accuracy of FNN-IMSP can be improved. Finally, FNN-IMSP is validated on a real WWTP. The experimental results indicate that the prediction model can achieve outstanding prediction performance.
ISSN:1934-1768
DOI:10.23919/CCC64809.2025.11178461