Based on the analysis of the error backpropagation algorithm, we propose an innovative training criterion of depth neural network for maximum interval minimum classification error. At the same time, the cross entropy and M 3 CE are analyzed and combined to obtain better results. Finally, we tested our proposed M3 CE-CEc on two deep learning standard databases, MNIST and CIFAR-10. The experimental results show that M 3 CE can enhance the cross-entropy, and it is an effective supplement to the cross-entropy criterion. M3 CE-CEc has obtained good results in both databases.

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In this paper, an adaptive fuzzy sliding mode control strategy with bound estimation is proposed to control the position of a micro-electro-mechanical systems gyroscope in the presence of model uncertainties and external disturbances. The proposed adaptive fuzzy sliding mode control system is composed of a fuzzy controller and a sliding mode controller. The sliding controller is designed to compensate for the approximation error between fuzzy controller and optimal fuzzy control law. The adaptation laws based on the Lyapunov analysis can adaptively adjust the fuzzy rules, thus guaranteeing the stability of the closed loop adaptive fuzzy control system. Moreover, an estimation mechanism is derived to identify the unknown upper bound of approximation error. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive fuzzy control scheme.

Deep learning algorithms have the advantages of clear structure and high accuracy in image recognition. Accurate identification of pests and diseases in crops can improve the pertinence of pest control in farmland, which is beneficial to agricultural production. This paper proposes a DCNN-G model based on deep learning and fusion of Google data analysis, using this model to train 640 data samples, and then using 5000 test samples for testing, selecting 80% as the training set and 20% as the test set, and compare the accuracy of the model with the conventional recognition model. Research results show that after degrading a quality level 1 image using the degradation parameters above, 9 quality level images are obtained. Use YOLO’s improved network, YOLO-V4, to test and validate images after quality level classification. Images of different quality levels, especially images of adjacent levels, are subjectively observed by human eyes, and it is difficult to distinguish the quality of the images. Using the algorithm model proposed in this article, the recognition accuracy is 95%, which is much higher than the basic 84% of the DCNN model. The quality level classification of crop disease and insect pest images can provide important prior information for the understanding of crop disease and insect pest images and can also provide a scientific basis for testing the imaging capabilities of sensors and objectively evaluating the image quality of crop diseases and pests. The use of convolutional neural networks to realize the classification of crop pest and disease image quality not only expands the application field of deep learning but also provides a new method for crop pest and disease image quality assessment.