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Distributed wavelet neural networks

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Abstract

Utilizing the wavelet theory, the wavelet coefficients with respect to translation and scaling factors can be obtained through the iteration of neural network, which effectively solves the window immobility problem of short time Fourier transform. Notice that, the centralized wavelet neural network is weak in the representation of signal integrity as localized information is lost in the case of the signal properties are large span time-domain and high frequency. To solve this problem, the learning algorithm of distributed wavelet neural networks (DWNNs) based on domain segment is proposed in this paper, where the time frequency characteristic of objective function is set as a constraint condition, while the time width, frequency, center of time domain and band center of the wavelet are employed to determine the translation and scaling parameters. Especially, wavelet networks are distributed into several orthogonal subnet by partitioning the information of input data, and the complexity of calculating for each nodes is thus decreased. Moreover, simulations are presented to demonstrate the advantages and efficiency of the proposed DWNNs.

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Acknowledgements

This work was supported in part by the National Natural Science Funds of China under Grant 61973277 and Grant 62073292 and in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR20F030004.

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Authors and Affiliations

  1. Department of Automation, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Wangzhuo Yang, Bo Chen & Li Yu

  2. Institute of Cyberspace Security, and Department of Automation, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Bo Chen

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  1. Wangzhuo Yang
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  2. Bo Chen
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  3. Li Yu
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Correspondence to Bo Chen.

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Yang, W., Chen, B. & Yu, L. Distributed wavelet neural networks. Appl Intell 52, 8735–8745 (2022). https://doi.org/10.1007/s10489-021-02892-4

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