Graph structure data are widely found in the real world. However， they often face a shortage of labeled data in practical applications. Methods for Few-Shot Learning （FSL） on graph data aim to classify data with a few labeled samples. Although these methods have good performance in Few-Shot Node Classification （FSNC） tasks， there are still the following problems： high-quality labeled data are difficult to obtain， generalization ability is insufficient in the parameter initialization process， the topology structure information in graph is not fully mined. To address these problems， a Few-Shot Node Classification model based Graph Data Augmentation （GDA-FSNC） was proposed. There are four modules in GDA-FSNC： a graph data pre-processing module based on structural similarity， a parameter initialization module， a parameter fine-tuning module， and an adaptive pseudo-label generation module. In the graph data pre-processing module， an adjacency matrix enhancement method based on structural similarity was used to obtain more graph structural information. In the parameter initialization module， to enhance the diversity of information during the model training process， a mutual teaching-based data augmentation method was used to make each model learn different patterns and features from the other models. In the adaptive pseudo-label generation module， appropriate pseudo-label generation techniques were selected automatically according to the characteristics of different datasets， thereby generating high-quality pseudo-label data. Experimental results on seven real datasets show that the proposed model performs better than the state-of-the-art FSL models such as Meta-GNN， GPN（Graph Prototypical Network）， and IA-FSNC （Information Augmentation for Few-Shot Node Classification） in classification accuracy. For example， compared to the baseline model IA-FSNC， The classification accuracy of the proposed model has been improved by at least 0.27 percentage points in the 2-way 1-shot setting of the small dataset and by at least 2.06 percentage points in the 5-way 1-shot setting of the large datasets. It can be seen that GDA-FSNC has better classification performance and generalization ability in few-shot scenarios.

Concerning the problem of identification of parameters and time delay for chaotic systems with unknown delay， a parameter identification model for time-delay chaotic systems based on temporal attention mechanism was proposed， namely Parameter Identification Neural Network with Temporal Attention （PINN-TA）. Firstly， the time delay identification was implemented by applying temporal attention mechanism to extract correlation features within system state sequences. Then， the algebraic equations of system parameters were formed by implicitly approximating system differential equation with the use of recurrent neural network. Finally， the roots of these equations were taken as the results of parameter identification. With typical time-delay chaotic systems including delay Logistic equation， Ikeda differential equation and Mackey-Glass chaotic system used as identificated objects， PINN-TA model was compared with multiple intelligent search algorithms in experiments. Simulation results show that PINN-TA model has the identification error of parameters and time delay 90.31% to 99.36% lower in comparison with existing intelligent search algorithms such as Artificial Raindrop Algorithm （ARA）， Hybrid Cuckoo Search （HCS）， Global Flower Pollination Algorithm （GFPA） and Cellular Whale Algorithm （CWA）， while the identification time of the proposed model is shortened to 18.59 to 19.43 ms. It can be seen that PINN-TA model can meet the accuracy and real-time requirements， and provides a feasible solution for identification of parameters and time delay for time-delay chaotic systems.