No abstract

Sea surface temperature (SST) is one of the most important parameters in air–sea interaction, and its accurate prediction is of great significance in the study of global climate change. However, SST is affected by heat flux, ocean dynamic processes, cloud coverage, and other factors, which means it contains linear and nonlinear components. Existing prediction models, especially single prediction models, cannot effectively handle these linear and nonlinear components in the meantime, degrading their accuracy concerning the prediction of SST. To remedy this weakness, this paper proposes a novel prediction model by the Lagrange multiplier method to combine the auto-regressive integrated moving average (ARIMA) model and the back propagation (BP) neural network model, where these two models have superior prediction performance for linear and nonlinear components, respectively. Moreover, the genetic algorithm is exploited to construct the genetic algorithm BP (GABP) neural network to further improve the performance of the proposed model. To verify the effectiveness of the proposed model, experiments predicting the SST based on historic time-series data are performed. The experiment results indicate that the mean absolute error (MAE) of the ARIMA-GABP model is only 0.3033 °C and the root mean square error (RMSE) is 0.3970 °C, which is better than the ARIMA model, BP neural network model, long short-term memory (LSTM) model, GABP neural network model, and ensemble empirical model decomposition BP model among various datasets. Therefore, the proposed model has superior and robust performance concerning predicting SST.

The time-variant Lyapunov equation (TVLE) has played an important role in many fields due to its ubiquity and many neural dynamics models have been developed to obtain the online solution of the TVLE. In prevalent methods, the gradient neural dynamics (GND) models suffer from the large residual error due to the lack of predictive computing, while the zero neural dynamics (ZND) models have large computing complexity because of the inverse of the mass matrix in models. To mitigate these deficiencies, an adaptive parameter containing the time-derivative of time-variant parameters in the TVLE is added to the GND model to form the adaptive GND (AGND) model, which enables the AGND model predictive computing as ZND models and inherits the free of matrix inverse from the GND models. Moreover, two strategies are proposed to design the accelerated AGND (AAGND) models that enjoy a faster convergence rate. The accuracy and the convergence rate of AAGND models are theoretically analyzed, indicating that AAGND models achieve zero residual error and a faster convergence rate. In addition, numerical simulations and two applications are provided to verify the theoretical analyses and the efficiency of AAGND models. The experimental results demonstrate that the AAGND model can solve the TVLE with high accuracy and have great potentialities in applications.

There are many well-known brands in today's automotive industry, such as BMW and Mercedes-Benz. On the contrary, there are abundant unpopular brands as well. Among numerous car brands in the global market, Tesla attracted almost everyone's attention in an extremely short period of time, notwithstanding the fact that it is a newly established company. This paper analyzes the financial status of Tesla and makes predictions about its future performance based on historical data to decide whether investing in Tesla is a reasonable idea. 3. Current financial performance Analyzing the historical data of Tesla gives an overall comprehension of the current financial status of the company. Table 1 shows several factors, which can demonstrate what financial condition the company was undergoing during the period from 2013 to 2017[2]. 3.1 Revenue and revenue growth rates Firstly, it can be observed from the table that the revenue is constantly increasing year by year. Additionally, remarkable rates of revenue growth remain every year. Nonetheless, the high revenue