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Related Experiment Video

Updated: Dec 19, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

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Pipelined chebyshev functional link artificial recurrent neural network for nonlinear adaptive filter.

Haiquan Zhao1, Jiashu Zhang

  • 1Key Laboratory of Signal and Information Processing of Sichuan Province, Southwest Jiaotong University, Chengdu 610031, China. hqzhao0815@yahoo.com.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 16, 2009
PubMed
Summary

A new pipelined Chebyshev functional link artificial recurrent neural network (PCFLARNN) offers improved computational efficiency and performance for signal prediction tasks. This novel nonlinear adaptive filter outperforms standard recurrent neural networks (RNNs) and pipelined RNNs (PRNNs).

Related Experiment Videos

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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

2.0K

Area of Science:

  • Artificial Intelligence
  • Signal Processing
  • Machine Learning

Background:

  • Standard recurrent neural networks (RNNs) have limitations in processing nonlinear and high-order input terms.
  • Existing pipelined recurrent neural networks (PRNNs) utilize linear inputs and first-order recurrent terms, failing to capture complex nonlinear dynamics.
  • There is a need for advanced adaptive filters that can efficiently handle complex signal prediction tasks.

Purpose of the Study:

  • To introduce a novel nonlinear adaptive filter based on a pipelined Chebyshev functional link artificial recurrent neural network (PCFLARNN).
  • To enhance computational efficiency and prediction performance compared to existing RNN and PRNN architectures.
  • To demonstrate the effectiveness of PCFLARNN in various signal prediction applications.

Main Methods:

  • Development of a PCFLARNN architecture comprising multiple small-scale Chebyshev functional link artificial recurrent neural network (CFLARNN) modules.
  • Implementation of a modified real-time recurrent learning algorithm for training the PCFLARNN.
  • Enhancement of nonlinearity within each CFLARNN module using Chebyshev functional expansion of input patterns.

Main Results:

  • PCFLARNN modules can be processed in a pipelined parallel fashion, significantly improving computational efficiency.
  • The use of Chebyshev functional expansion allows PCFLARNN to utilize high-order input terms, outperforming linear RNNs.
  • Computer simulations confirm superior performance of PCFLARNN over PRNN and RNN in nonlinear colored signal prediction, nonstationary speech signal prediction, and chaotic time series prediction.

Conclusions:

  • The proposed PCFLARNN offers a significant advancement in nonlinear adaptive filtering.
  • PCFLARNN demonstrates enhanced computational efficiency and superior prediction accuracy for complex time series.
  • The Chebyshev functional expansion effectively introduces nonlinearity, improving filter performance and potentially reducing input signal requirements.