Application of a recursive subspace identification method to derive a state space model for a synchronous machine is described in this paper. Simulation studies show the effectiveness of such an algorithm to identify on-line a synchronous machine model over a wide range of operating conditions and disturbances. The model provides a foundation for further study on a MIMO adaptive power system st...

space-time adaptive processing is used for clutter mitigation in airborne radars. in this paper, deterministic clutter subspace and direct data domain methods are introduced due to the fact that space-time adaptive processing algorithm requires estimating covariance matrix, limited training data and high complexity in processing. in first proposed method, filter coefficients are calculated by e...

Subspace identification methods, such as canonical variate analysis (CVA), are noniterative tools suitable for the state-space modeling of multi-input, multi-output processes, e.g., industrial using input-output data. To learn nonlinear system behavior, kernel subspace techniques commonly used. However, issue design must be given more attention because type can influence kind nonlinearities tha...

The subspace-based state-space system identification techniques have been applied to different industrial applications with success for more than two decades [36, 10, 4, 1, 8, 27, 7, 14]. A quick look at these contributions leads to the conclusion that these accurate results are mainly obtained with collected measurements of good quality. It is now well-known that using persistently exciting in...

In this paper, we present a subspace method for learning nonlinear dynamical systems based on stochastic realization, in which state vectors are chosen using kernel canonical correlation analysis, and then state-space systems are identified through regression with the state vectors. We construct the theoretical underpinning and derive a concrete algorithm for nonlinear identification. The obtai...

We give a general overview of the state-of-the-art in subspace system identification methods. We have restricted ourselves to the most important ideas and developments since the methods appeared in the late eighties. First, the basis of linear subspace identification are summarized. Different algorithms one finds in literature (Such as N4SID, MOESP, CVA) are discussed and put into a unifying fr...

Stochastic realization theory provides a natural theoretical background for recent identification methods, called subspace methods, which have shown superior performance for multivariable state-space model-building. The basic steps of subspace algorithms are geometric operations on certain vector spaces generated by observed input-output time series which can be interpreted as “sample versions”...

State-space modeling provides a powerful tool for system identification and prediction. In linear state-space models the data are usually assumed to be Gaussian and the models have certain structural constraints such that they are identifiable. In this paper we propose a non-Gaussian state-space model which does not have such constraints. We prove that this model is fully identifiable. We then ...