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A Quantitative Measure to Evaluate Competing Designs for Nonlinear Dynamic Process Identification
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rollins, Derrick K. Pacheco, Liza Bhandari, Nidhi |
| Copyright Year | 2005 |
| Abstract | The strategy for the collection of information (i.e., data) for model development is called experimental design. Optimal design seeks to maximize the information content under constraints of time and sampling. For the building of “gray-box” dynamic predictive nonlinear models, the dominant strategy has been the method of pseudo-random sequences (PRS). However, this work demonstrates the superiority of statistical design of experiments (SDOE) through a quantitative measure of information content, the D-optimal criterion. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.public.iastate.edu/~drollins/D-optimality_w02-FINAL.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Design of experiments Experiment Fanconi-Bickel Syndrome Nonlinear system Optimal design Plateau dynamic:Pres:Pt:Respiratory system:Qn Procedural reasoning system Pseudo brand of pseudoephedrine Pseudorandomness Sampling (signal processing) Self-information |
| Content Type | Text |
| Resource Type | Article |
