<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Eckhard</style></author><author><style face="normal" font="default" size="100%">H. Hjalmarsson</style></author><author><style face="normal" font="default" size="100%">C. Rojas</style></author><author><style face="normal" font="default" size="100%">M. Gevers</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mean-Squared Error Experiment Design for Linear Regression Models</style></title><secondary-title><style face="normal" font="default" size="100%">16th {IFAC} Symposium on System Identification</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">IFAC</style></publisher><pub-location><style face="normal" font="default" size="100%">Brussels</style></pub-location><pages><style face="normal" font="default" size="100%">1629–1634</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This work solves an experiment design problem for a linear regression problem using a reduced order model. The quality of the model is assessed using a mean square error measure that depends linearly on the parameters. The designed input signal ensures a predefined quality of the model while minimizing the input energy.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes></record></records></xml>