{"product_id":"9789054876489","title":"Model Fitting in Frequency Domain Imposing Stability of the Model","description":"\u003cp\u003eSystem identification is a powerful technique for constructing accurate models of complex systems from noisy input-output observations. It mainly consists of three basic steps that are interrelated:\u003c\/p\u003e\u003cp\u003e(1) design of the experiment;\u003c\/p\u003e\u003cp\u003e(2) choice of a parametric model (black box or physical laws);\u003c\/p\u003e\u003cp\u003e(3) and the estimation of the model parameters from noisy measurements.\u003c\/p\u003e\u003cp\u003eAccording to the intended goal of the identification experiment - physical interpretation, simulation, prediction, or control - some additional properties may be imposed on the identified model such as reciprocity, passivity, stability, ...\u003c\/p\u003e\u003cp\u003eThis thesis presents both theoretical (theorems) and practical (algorithms) contributions to the third step of an identification experiment: the estimation of guaranteed stable models from noisy data. A two step procedure is proposed: in the first step an unconstrained model is identified from the noisy measurements. Next, if unstable, the unstable model is in a second step approximated by a guaranteed stable model by adding an appropriate delay to the target function. The final result is a stable model with bias and noise uncertainty bounds that is useful in open loop simulation or prediction applications.\u003c\/p\u003e","brand":"ASP - Academic \u0026 Scientific Publishers","offers":[{"title":"Default Title","offer_id":47058179653872,"sku":"9789054876489","price":44.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9789054876489_p0.jpg?v=1763670497","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9789054876489","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}