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Intelligent analysis system for signal processing tasks based on LSTM recurrent neural network algorithm

  • S.I.: Machine Learning based semantic representation and analytics for multimedia application
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Abstract

In order to improve the effectiveness of signal processing, according to the actual needs of signal processing and the current problems in signal processing, this paper introduces an improved LSTM recurrent neural network algorithm to construct the signal processing algorithm. Moreover, this paper sets up the functional structure of this paper based on the neural network model structure, and builds an intelligent analysis system for signal processing tasks based on the LSTM recurrent neural network algorithm. In addition, this paper analyzes the system algorithm flow in detail, and combines experimental research to carry out the effectiveness of the system constructed in this paper, and conducts quantitative analysis from two aspects of signal threshold prediction and signal processing effect. Finally, this paper conducts experimental results research with the support of mathematical statistics methods. From the research point of view, it can be known that the system constructed in this paper has good signal processing functions.

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Funding

The research is funded by Key Scientific Research Project of Xuchang University - Design of intelligent IOT management and control system at substation side based on BIM model and edge calculation (No. 2021ZD004).

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Authors and Affiliations

  1. School of Electrical and Mechanical Engineering, Xuchang University, Xuchang, 461000, China

    Ya Zhou

  2. Internet Department, State Grid Xuchang Power Supply Company, Xuchang, 461000, China

    Xiaobo Jiao

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  1. Ya Zhou
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  2. Xiaobo Jiao
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Correspondence to Ya Zhou.

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Zhou, Y., Jiao, X. Intelligent analysis system for signal processing tasks based on LSTM recurrent neural network algorithm. Neural Comput & Applic 34, 12257–12269 (2022). https://doi.org/10.1007/s00521-021-06478-6

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  • DOI: https://doi.org/10.1007/s00521-021-06478-6

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