A global model structure is developed for parametrization and identification of a general class of Linear Parameter-Varying (LPV) systems. By using a fixed orthonormal basis function (OBF) structure, a linearly parametrized model structure follows for which the coefficients are dependent on a scheduling signal. An optimal set of OBFs for this model structure is selected on the basis of local li...

Abstract We consider a general twisted shift-invariant system, $V^{t}(\mathcal {A})$ , consisting of translates countably many generators and study the problem obtaining characterization for system to form frame sequence or Riesz sequence. illustrate our theory with some examples. In addition these results, we dual also obtain an orthonormal from given translates.

In this paper the implementation of the SVD{updating algorithm using orthonormal { rotations is presented. An orthonormal {rotation is a rotation by an angle of a given set of {rotation angles (e.g. the angles i = arctan2 ?i) which are choosen such that the rotation can be implemented by a small amount of shift{add operations. A version of the SVD{updating algorithm is used where all computatio...

| This paper develops a general and very simple construction for complete orthonormal bases for system identiica-tion. This construction provides a unifying formulation of many previously studied orthonormal bases since the common FIR and recently popular Laguerre and two-parameter Kautz model structures are restrictive special cases of the construction presented here. However, in contrast to t...

today, companies need to make use of appropriate patterns such as supply chain management system to gain and preserve a position in competitive world-wide market. supply chain is a large scaled network consists of suppliers, manufacturers, warehouses, retailers and final customers which are in coordination with each other in order to transform products from raw materials into finished goods wit...

Nonlinear system identiication is often solved by determining a set of coeecients for a nite number of xed nonlinear basis functions. However, if the input data is drawn from a high{dimensional space, the number of required basis functions grows exponentially with dimension, and this has led many authors to consider subset model selection techniques. In this paper we describe a one hidden layer...