Time series and neural networks have profoundly affected the fields of artificial intelligence and machine learning. It can be applied to tasks such as image recognition and target detection with high accuracy. In order to solve the shortcomings of the existing time series neural network animation image recognition stability research, this paper discusses the time series model stationarity, animation image image feature extraction and the functional equation of neural network loss. The dataset and parameter settings for sequential neural network animation image recognition applications are briefly introduced. And the work flow design of the time series neural network animation image recognition system structure model is discussed, and finally the stability of the time series neural network in animation image recognition is compared with the machine learning SVF, FCN model and RFC model. The experimental data show that the average absolute error of the time series neural network model in animation image recognition is small. When the number of iterations is 50-250, the average absolute error of the time series neural network model is less than 2%, while the machine learning SVF, FCN The average absolute error of the model and the RFC model is greater than 10%, so it is verified that the time series neural network model has a faster convergence speed and higher stability in animation image recognition.

Khadijah Viju. Stability of Neural Networks Dependent on Time Series in Anime Image Recognition. International Journal of Neural Network (2021), Vol. 2, Issue 3: 45-52. https://doi.org/10.38007/NN.2021.020306.

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