State space models (SSMs) are important for multi-variable performance analysis and controller design of aero-engines. In order to solve the problems traditional state modeling methods that rely on component-level (CLMs) cannot be carried out in real time, an aero-engine method based adaptive forgetting factor online sequential extreme learning machine (AFOS-ELM) is proposed this paper. The str...

Battery Management System (BMS) requires an indefinite accurate model. With an aging model, the lifetime of a battery can be precisely predicted. The mathematical model in terms of state variables is presented in this preliminary work involving smart BMS system. This work is crucial as the state space model is able to mimic the complex dynamic behavior of a battery system. A numerical case stud...

In this paper, an extended linearized neural state space (ELNSS) topology is proposed, where an ELNSS based modeling and cont rol strategy for a class of nonlinear systems is presented. In terms of the modeling, the extended Kalman filter (EKF) algorithm is used to train the parameters inside the ELNSS model, where a high order correlation method is applied to validate the estimated model. This...

State-space modeling and Bayesian analysis are both active areas of applied research in fisheries stock assessment. Combining these two methodologies facilitates the fitting of state-space models that may be nonlinear and have non-normal errors, and hence it is particularly useful for the modeling of fisheries dynamics. Here, this approach is demonstrated by fitting a non-linear surplus product...

With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessly. Whenever critical temperatures cannot be measured economically on a sensor base, model lends itself estimate those unknown quantities. Thermal models for are usually required both, real-time capable high estimation...