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A measurement fusion method for nonlinear system identification using a cooperative learning algorithm.

Youshen Xia1, Mohamed S Kamel

  • 1College of Mathematics and Computer Science, Fuzhou University, 350002 Fuzhou, China. ysxia2001@yahoo.com

Neural Computation
|April 21, 2007
PubMed
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This study introduces a novel measurement fusion method for estimating nonlinear noisy systems. The approach minimizes approximation and noise errors, yielding more accurate models than existing techniques.

Area of Science:

  • Systems Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Accurate identification of general nonlinear noisy systems is crucial for many engineering applications.
  • Existing methods often struggle to minimize both approximation and noise errors simultaneously.
  • Robust least squares support vector machine (LS-SVM) offers a promising approach for nonlinear function estimation.

Purpose of the Study:

  • To propose a novel measurement fusion method for estimating predictor functions in general nonlinear noisy systems.
  • To enhance the accuracy and robustness of system identification by minimizing approximation and noise errors.
  • To develop a cooperative learning algorithm for efficient implementation of the proposed method.

Main Methods:

  • A measurement fusion technique is employed to optimally fuse observed data.

Related Experiment Videos

  • Fused data are then used in a nonlinear function estimator based on a robust least squares support vector machine (LS-SVM).
  • A cooperative learning algorithm is developed to implement the measurement fusion method.

    • Main Results:

    • The proposed method demonstrates a smaller mean square error compared to standard LS-SVM function estimation.
    • The cooperative learning algorithm guarantees global convergence to the optimal measurement fusion function estimate.
    • Application to ARMA and spatio-temporal signal modeling shows improved model accuracy.

    Conclusions:

    • The proposed optimal measurement fusion method effectively identifies general nonlinear noisy systems with reduced errors.
    • The cooperative learning algorithm provides a robust and efficient implementation.
    • This approach offers a significant advancement in system modeling and identification accuracy.