A New Artificial Intelligence Recognition Technology Based On Convolutional Neural Networks

With the continuous development of social science and technology, artificial intelligence identification has been widely used and plays a very important role in some special fields. Convolutional neural network has a good effect in image processing, so it is widely used in intelligent recognition scenario. Activation functions can help convolutional neural network better understand and fit complex function models, It is necessary to design an efficient activation function. This paper proposes a new convolutional neural network model based on improved activation function usage patterns, and the performances of three common used activation functions, including sigmoid function, tanh function and relu function, in centralized and decentralized training methods are detailed analyzed respectively. The experiment results show that the effect of repeated training with different activation functions is better than that of single linear rectification function in recognition accuracy and recognition of special cases, and the recognition speed is obviously faster than the traditional model. Furthermore, under the same activation function, when the number of training rounds and the training amount are small, the expected accuracy of centralized training is lower compared with that of decentralized training, but the detection accuracy is improved due to the detection mechanism.