Forward Dynamic Identification

Keywords— Rehabilitation Robot, Modeling, Neural MEMO NARX Model, Forward Dynamic Identification. 

Influenced by the mind of forward modeling problems, it is easily directed to adopt complicated model classes so as to capture various complex physical mechanisms. However, the more complicated the model class is utilized, the more uncertain parameters are normally induced unless extra mathematical constraints are imposed. In the former case, the model output may not necessarily be accurate even if the model well characterizes the physical system since the combination of the many small errors from each uncertain parameter can induce a large output error. In the latter case, it is possible that the extra constraints induce substantial errors. Therefore, it is important to use a proper model class for system identification purpose. In this chapter, the Bayesian model class selection approach is introduced and applied to select the most plausible/suitable class of mathematical models representing a static or dynamical (structural, mechanical, atmospheric,...) system (from some specified model classes) by using its response measurements. This approach has been shown to be promising in several research areas, such as artificial neural networks [164,297], structural dynamics and model updating [23], damage detection [150] and fracture mechanics [151], etc. 

The fact that the CPMD was a milestone step forward in realistic simulations of materials, at various thermodynamics conditions, can be easily seen by the number of publications in first principles molecular dynamics (FPMD) before and after 1985, i.e. after the original CPMD publication [21]. Indeed, the original Car-Parrinello publication has more than 6500 citations in 2014 (source ISI Web of Science), and, to acknowledge the importance of the method, the international PACS (Physics and Astronomy Classification Scheme) introduced in 1996 a new identification number, 71.15. Pd, to classify Car-Parrinello related publications. Since then, the method has been applied to a wide variety of materials, ranging from solids, to liquids and to biological systems [33]. 

Feldman and OrUkowski s second point has to do with change and how practice theoretical studies can contribute to stimulate changes in practices by highlighting the micro-dynamics of the practices. It is obvious that the analytic identification of dysfunctionalities within practices can provide a good starting point for interventions. The question is whether the practice theoretical approach has potentiality beyond the mere analytic identification of micro-dynamic dysfunctionalities. It is not possible to settle this question here, but I will point to an interesting research project conducted by Rabinow and Bennett (2012, 2013) in synthetic biology. I leave it as an open question whether Rabinow and Bennett s approach describes a way forward for practice theoretical interventions. 

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