Objective： In order to meet the needs of the edge computing of the Internet of Things, a recurrent neural network algorithm was introduced for the first time in this paper, and an intelligent real-time classification and recognition system was constructed to conduct research on food packaging images. Methods： To build a simulation experiment test model, firstly preprocessed the image data set, de-redundantize, grayscale, and normalized the two-dimensional image, and finally input the time-sequential data in parallel. Using a typical memristor as the research object of realizing hardware RNN, the nonlinear function of memristor was used to construct the mapping layer of parallel array reserve pool neural network. the ridge regression algorithm was used to solve the problems of overfitting in the training process. Results: The classification accuracy of the food packaging data set was as high as 98.59%. Conclusion： The system reduces the number of traditional neural network layers, reduces the training cost, and realizes high-precision real-time online recognition of time series signals.
"Research on intelligent real-time identification system of traffic signs based on improved recurrent neural network algorithm,"
Food and Machinery: Vol. 39:
9, Article 17.
Available at: https://www.ifoodmm.cn/journal/vol39/iss9/17
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