Simultaneous identification of process structure, parameters and time-delay based on non-negative garrote
In practice, the model structure, parameters and time-delay of the actual process may vary simultaneously. However, the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical application. In view of the fact that variable selection procedure can ensure a compact model with robust input-output relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure, parameters and time-delay, non-negative garrote (NNG) algorithm is introduced and applied to system identification and the corresponding procedures are presented. The application of NNG variable selection algorithm to the identification of single input single output (SISO) system, multiple input multiple output (MIMO) system and Wood-Berry tower industry are investigated. The identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square (OLS) algorithms. The derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure, parameters and time-delay with high precision.
KeywordsModel structure model parameter time-delay non-negative garrote variable selection
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