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A neuron fuzzy identification system based on a complex nonlinear mathematical model

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Abstract

During the implementation process of identification systems, multiple factors need to be considered at the same time. Due to the large amount of calculation and the randomness of the signal, the automatic identification capability of the machine is currently poor, especially for continuous signal. In order to solve this problem, a neuron fuzzy identification system was described based on a complex nonlinear mathematical model, which was designed from both hardware and software aspects of the system. The hardware architecture diagram was constructed based on the S3C:2440 microprocessor. The main modules include the power module, acquisition module, storage module, and output module. The DSP/BIOS system was used to construct the software framework diagram of the neuron fuzzy identification system to describe the identification process of video data stream, image data stream and control signal data. The software algorithm was designed based on the neuron fuzzy theory to establish a fuzzy similarity matrix and the best identification result was found by the maximum and minimum methods. The experimental results show that the designed system has higher recognition ability. When the frame length reaches 40 frames, the recognition rate increases to a larger value, the recognition rate is 75%, and the information is more accurate.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

In this paper, the research was supported by Chinese Natural Science Foundation (Project No. 11361048), Yunnan Natural Science Foundation (Project No. 2017FH001-014) and Qujing Normal University Natural Science Foundation (Project No. ZDKC2016002).

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Authors and Affiliations

  1. College of Mathematics and Statistics, Qujing Normal University, Qujing, 655011, China

    Hongying Luo

  2. Institute of Applied Mathematics, Qujing Normal University, Qujing, 655011, China

    Jun Liu & Xuebin Li

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  1. Hongying Luo
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Correspondence to Jun Liu.

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Luo, H., Liu, J. & Li, X. A neuron fuzzy identification system based on a complex nonlinear mathematical model. Wireless Netw 28, 2299–2311 (2022). https://doi.org/10.1007/s11276-021-02738-4

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