Prediction of seawater pH by bidirectional gated recurrent neural network with attention under phase space reconstruction:case study of the coastal waters of Beihai,China

Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.

Wind power forecasting based on improved variational mode decomposition and permutation entropy

Due to the significant intermittent,stochastic and non-stationary nature of wind power generation,it is difficult to achieve the desired prediction accuracy.Therefore,a wind power prediction method based on improved variational modal decomposition with permutation entropy is proposed.First,based on the meteorological data of wind farms,the Spearman correlation coefficient method is used to filter the meteorological data that are strongly correlated with the wind power to establish the wind power prediction model data set;then the original wind power is decomposed using the improved variational modal decomposition technique to eliminate the noise in the data,and the decomposed wind power is reconstructed into a new subsequence by using the permutation entropy;with the meteorological data and the new subsequence as input variables,a stacking deeply integrated prediction model is developed;and finally the prediction results are obtained by optimizing the hyperparameters of the model algorithm through a genetic algorithm.The validity of the model is verified using a real data set from a wind farm in north-west China.The results show that the mean absolute error,root mean square error and mean absolute percentage error are improved by at least 33.1%,56.1%and 54.2%compared with the autoregressive integrated moving average model,the support vector machine,long short-term memory,extreme gradient enhancement and convolutional neural networks and long short-term memory models,indicating that the method has higher prediction accuracy.

Data Driven Model-Free Adaptive Control Method for Quadrotor Trajectory Tracking Based on Improved Sliding Mode Algorithm

In order to solve the problems of dynamic modeling and complicated parameters identification of trajectory tracking control of the quadrotor,a data driven model-free adaptive control method based on the improved sliding mode control(ISMC)algorithm is designed,which does not depend on the precise dynamic model of the quadrotor.The design of the general sliding mode control(SMC)algorithm depends on the mathematical model of the quadrotor and has chattering problems.In this paper,according to the dynamic characteristics of the quadrotor,an adaptive update law is introduced and a saturation function is used to improve the SMC.The proposed control strategy has an inner and an outer loop control structures.The outer loop position control provides the required reference attitude angle for the inner loop.The inner loop attitude control ensures rapid convergence of the attitude angle.The effectiveness and feasibility of the algorithm are verified by mathematical simulation.The mathematical simulation results show that the designed model-free adaptive control method of the quadrotor is effective,and it can effectively realize the trajectory tracking control of the quadrotor.The design of the controller does not depend on the kinematic and dynamic models of the unmanned aerial vehicle(UAV),and has high control accuracy,stability,and robustness.