Application of Ship Identification Number Recognition based on Hopfield Neural Network

Yongquan Xie ； Zhenyu Qu ； Di Wu

《International Core Journal of Engineering》 7卷4期 (2021/04) Pp. 224-238

https://doi.org/10.6919/ICJE.202104_7(4).0031

摘要

In recent years, artificial intelligence is hot, applied in various fields and industries, and has greatly promoted the development of various industries. This paper is based on the Hopfield neural network application to derive the application of ship identification number identification. Hopfield neural network is a recursive combination of storage and binary systems, and ultimately ensures that the network tends to a stable state of single-layer structure by reducing the energy characteristics of recursion. In this paper, the letters and numbers of the ship identification number are designed in the standard matrix, noise (fixed or random noise) interference is added in the later stage, and the noise matrix is processed to identify the fuzzy ship identification number under different noise intensity. The final conclusion of this paper is that under a certain range of noise intensity interference, Hopfield neural network can still identify the letters and numbers of the ship identification number more accurately. The system has strong anti-interference ability and certain practical application value.However, when the noise intensity exceeds a certain range, it is difficult for the system to identify the ship identification number accurately and effectively.

關鍵字

Hopfield Neural Network ； Ship Identification Number ； Letter Recognition ； Digital Recognition ； Noise Intensity

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