Abstract. There are many factors affecting dam deformation, and the time series of deformation data is directly modeled without considering the seasonality and periodicity of each influencing factor, the Ensemble Empirical Mode Decomposition (EEMD) and the Seasonal Autoregressive Integrated Moving Average (SARIMA) is proposed for prediction in this paper. Firstly, the time series of deformation data is decomposed by EEMD, which weakens its volatility to some extent, and decomposes various factors affecting dam deformation, so as to obtain a series of Intrinsic Mode Function (IMF) with different frequencies; secondly, according to the seasonal characteristics and periodic characteristics of each IMF, the SARIMA model was established respectively for rolling prediction; thirdly, the final forecast results can be obtained by superimposing the forecast results of each IMF. It is verified by experiments and compared with Gray Model, Kalman Filter Model and SARIMA model that EEMD-SARIMA model has higher prediction accuracy, and it has better fitting degree, which means that it is an effective method for dam deformation prediction.
In this paper, the problem of adaptive control is investigated for a class of non-strict feedback stochastic nonlinear systems with input delay. First, the effect of the input delay is eliminated by constructing an appropriate auxiliary system with the same order as the considered system. Then, with the help of the backstepping technique and the structural characteristics of the radial basis function (RBF) neural network (NN), an adaptive neural control scheme is extended to non-strict feedback stochastic nonlinear systems with input delay, in which uncertain nonlinear functions are approximated by RBF NN. Furthermore, the proposed adaptive controller ensures that all the closed-loop signals remain bounded in probability. Finally, two examples are provided to confirm the effectiveness of the designed strategy.
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