This chapter is devoted to different types of optimal perturbations (OP), deterministic, stochastic, OP in an invariant subspace, and simultaneous stochastic perturbations (SSP). The definitions of OPs are given. It will be shown how the OPs are important for the study on the predictability of behavior of system dynamics, generating ensemble forecasts as well as in the design of a stable filter. A variety of algorithm-based SSP methodology for estimation and decomposition of very high-dimensional (Hd) matrices are presented. Numerical experiments will be presented to illustrate the efficiency and benefice of the perturbation technique.
Part of the book: Perturbation Methods with Applications in Science and Engineering
This chapter proposes a new approach for the design of an adaptive filter (AF), which is based on an artificial neural network (NN) structure for estimating the system state. The NNs are now widely used as a technology offering a way to solve complex and nonlinear problems such as time-series forecasting, process control, parameter state estimation, and fault diagnosis. The proposed NN-based adaptive filtering (NNAF) is designed by considering the filtering algorithm as an input–output system and two-stage optimization procedure. The first concerns a learning process where the weights of the NNAF are estimated to minimize the error between the filtered state and the state samples generated by a numerical model. The adaptation is carried out next to minimize the mean prediction error (MPE) of the system outputs (error between the observations and the system output forecast) subject to the coefficients associated with the estimated NN weights. Simulation results for different numerical models, especially for state estimation of the chaotic Lorenz system as well as for the ocean current at different deep layers which is important for renewable energy device placements, are presented to show the efficiency of the NNAF.