The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...

A two-stage identification algorithm is introduced for tracking the parameters in time-varying Hammerstein-Wiener systems. The Kalman filtering and parameter separation technique are employed proposed algorithm. convergence analysis of this provided. It shown that can guarantee boundedness estimation error. Four simulation examples, including a practical system application electric arc furnace,...

Financial and economic time series can feature locally explosive behaviour when bubbles are formed. We develop a time-varying parameter model that is capable of describing this in data. Our proposed dynamic be used to predict the emergence, existence burst bubbles. adopt flexible observation driven specification allows for different bubble shapes behaviour. establish stationarity, ergodicity, b...

In this paper, we establish a unified framework for subspace identification (SID) of linear parameter-varying (LPV) systems to estimate LPV state–space (SS) models in innovation form. This enables us derive novel SID schemes that are extensions existing time-invariant (LTI) methods. More specifically, the open-loop, closed-loop, and predictor-based data-equations (input–output surrogate forms S...