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Related Experiment Video

Updated: Jul 7, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

A recurrent fuzzy-neural model for dynamic system identification.

P A Mastorocostas¹, J B Theocharis

¹Dept. of Electr. & Comput. Eng., Aristotelian Univ. of Thessaloniki.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 2, 2008

Summary

This summary is machine-generated.

A new Dynamic Fuzzy Neural Network (DFNN) model and its training algorithm (D-FUNCOM) offer efficient dynamic system identification. This approach shows superior performance compared to existing methods in temporal tasks.

Related Experiment Videos

Last Updated: Jul 7, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Area of Science:

* Computational intelligence
* Artificial neural networks
* Fuzzy systems

Background:

* Dynamic system identification is crucial for understanding and controlling complex processes.
* Traditional fuzzy models often struggle with capturing temporal dynamics effectively.
* Recurrent neural networks offer potential for modeling time-dependent behavior but can be complex to train.

Purpose of the Study:

* To introduce a novel dynamic fuzzy model, the Dynamic Fuzzy Neural Network (DFNN).
* To develop an efficient training algorithm, the Dynamic-Fuzzy Neural Constrained Optimization Method (D-FUNCOM).
* To evaluate the DFNN model's performance in temporal system identification tasks.

Main Methods:

* Development of the DFNN model featuring recurrent TSK rules with static premise/defuzzification and recurrent neural network consequents.
* Implementation of the D-FUNCOM algorithm, a constrained optimization-based learning method for network training.
* Application of the DFNN model to temporal problems, including NARMA process modeling and noise cancellation.

Main Results:

* The DFNN model demonstrated favorable performance in temporal system identification tasks.
* Comparative analysis showed the DFNN model outperforming static, dynamic, and existing recurrent fuzzy models.
* The D-FUNCOM algorithm proved effective for training both locally and fully recurrent networks.

Conclusions:

* The proposed DFNN model offers an efficient and effective approach for dynamic system identification.
* The D-FUNCOM learning algorithm provides a robust method for training complex recurrent fuzzy networks.
* DFNN presents a promising alternative for applications requiring accurate modeling of temporal dynamics.