The purpose of this study is to improve the prediction accuracy of wind speed. The wind speed has the characteristics of unstable, non-stationary, and non-linear, so it is difficult to predict the wind speed. This study proposes a prediction model based on the complementary ensemble empirical mode decomposition-sample entropy and multiple echo state network (ESN) with Gauss–Markov fusion for wind speed. The proposed prediction model consists of the following steps: (a) using the complementary ensemble empirical mode decomposition algorithm, it decomposes the initial wind speed time series and obtains some components with different scales, and (b) using the sample entropy algorithm, it determines the complexity of each component. The components whose entropy is larger than the original wind speed remain unchanged, while the components whose entropy is smaller than the original wind speed are merged into one. The reconstructed component greatly reduces the number of prediction models. (c) After reconstruction, the ESN has good regression prediction ability, so it is chosen as the prediction model of each component. The gray wolf optimization algorithm is introduced to optimize the parameters of the ESN. (d) The Gauss–Markov algorithm is adopted to fuse the predicted values of multiple ESN models. The variance of the predicted value obtained using the Gauss–Markov fusion is less than that of the single ESN model, which significantly increases the prediction accuracy. In order to verify the prediction performance of the proposed model, the actual ultra-short-term and short-term wind speed sample data are compared. At the same time, seven prediction models are chosen as the comparison model. Finally, through the comparison of the prediction error and its histogram distribution, eight performance indicators, Pearson’s correlation coefficient, and Diebold–Mariano test, all the results show that the proposed prediction model has high prediction accuracy.

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

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A scheduling strategy of variable sampling period combined with deadband feedback for networked control system is proposed. Variable sampling period algorithm can allocate a reasonable sampling period to each controlled loop according to the network utilization and packet transmission time. Deadband feedback algorithm can alleviate network congestion by appropriately adjusting the packets in the network when the networked control system cannot be scheduled. According to the actual overload and utilization of the network, the designed scheduling strategy dynamically adjusts the sampling period and priority, and improves the performance of the system combined with deadband feedback. Based on the TrueTime platform, the proposed scheduling strategy is verified on a three controlled loops networked control system with interference nodes and limited network resources. The simulation results demonstrate that the designed scheduling strategy can overcome the uncertainty of the upper bound of network resources, improve output control performance, reduce integral absolute error value of the controlled loop, and shorten the packet transmission time. The overall control performance of the system is improved. The designed scheduling strategy is effective.