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Related Experiment Videos

Prediction and identification using wavelet-based recurrent fuzzy neural networks.

Cheng-Jian Lin, Cheng-Chung Chin

    IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
    |October 27, 2004
    PubMed
    Summary

    A novel wavelet-based recurrent fuzzy neural network (WRFNN) effectively predicts nonlinear dynamic systems. This WRFNN model offers improved accuracy with fewer parameters compared to existing methods.

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    Area of Science:

    • Artificial Intelligence
    • Computational Neuroscience
    • Control Systems

    Background:

    • Nonlinear dynamic systems present significant challenges in accurate prediction and identification.
    • Traditional fuzzy models and neural networks have limitations in capturing complex temporal dynamics.

    Purpose of the Study:

    • To introduce a Wavelet-based Recurrent Fuzzy Neural Network (WRFNN) for enhanced prediction and identification of nonlinear dynamic systems.
    • To integrate Takagi-Sugeno-Kang (TSK) fuzzy logic with Wavelet Neural Networks (WNN) for improved system modeling.

    Main Methods:

    • Developed a WRFNN model by incorporating feedback connections into a feedforward Wavelet-based Fuzzy Neural Network (WFNN).
    • Utilized nonorthogonal, compactly supported functions as wavelet bases within the WNN.

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  • Implemented an online learning algorithm encompassing both structure learning (based on degree measure) and parameter learning (gradient descent).

    • Main Results:

    • The WRFNN model demonstrated superior performance in computer simulations.
    • Achieved a smaller root-mean-square (rms) error compared to other existing methods.
    • Required fewer adjustable parameters for effective system modeling.

    Conclusions:

    • The proposed WRFNN model is effective for nonlinear dynamic system prediction and identification.
    • The integration of wavelet transforms and recurrent fuzzy logic enhances modeling capabilities.
    • WRFNN offers a more parsimonious and accurate approach to complex system analysis.