Variance analysis of identified linear MISO models having spatially correlated inputs, with application to parallel Hammerstein models

详细信息    查看全文

• 作者：Pouria Ramazi ^{p.ramazi@gmail.com" class="auth_mail}Author Vitae ; Hå ; kan Hjalmarsson¹ ; ^{hakan.hjalmarsson@ee.kth.se" class="auth_mail}Author Vitae ; Jonas Må ; rtensson ^{jonas.martensson@ee.kth.se" class="auth_mail}Author Vitae
• 关键词：System identification ; Asymptotic variance ; Parallel MISO Hammerstein models ; Linear MISO models ; Bayesian networks
• 刊名：Automatica
• 出版年：June 2014
• 年：2014
• 卷：50
• 期：6
• 页码：1675-1683
• 全文大小：1294 K

文摘

This contribution concerns variance analysis of linear multi-input single-output models when the inputs are temporally white but where different inputs may be correlated. An expression is provided for the variance of a linearly parametrized estimate of the frequency response function from one block, i.e. from one input to the output. In particular, this expression reveals that the variance increases in one block when the number of estimated parameters in another block is increased, but levels off when the number of parameters in the other block reaches the number of parameters in the block in question. It also quantifies exactly how correlation between inputs affects the resulting accuracy and a graphical representation is provided for this purpose. The results are applicable to parallel MISO Hammerstein models when the nonlinearities are known and generalize an existing variance expression for this type of model.