Advances in Data-driven Optimization of Parametric and Non-parametric Feedforward Control Designs with Industrial Applications

2009 ◽  
pp. 167-184
Author(s):  
Rob Tousain ◽  
Stan van der Meulen
Keyword(s):  
Feedforward Control ◽  
Industrial Applications ◽  
Data Driven ◽  
Control Designs ◽  
Non Parametric

scholarly journals Feedforward control design for shaking table by Data driven control considering control input limitation

2020 ◽  
Vol 53 (2) ◽  
pp. 9011-9016
Author(s):  
Shinji Ishihara ◽  
Koichi Tahara ◽  
Koji Hironaka
Keyword(s):  
Feedforward Control ◽  
Control Design ◽  
Shaking Table ◽  
Data Driven ◽  
Control Input

Data-Driven Control and Process Monitoring for Industrial Applications—Part I

2014 ◽  
Vol 61 (11) ◽  
pp. 6356-6359 ◽  
Author(s):  
Shen Yin ◽  
Huijun Gao ◽  
Okyay Kaynak
Keyword(s):  
Process Monitoring ◽  
Industrial Applications ◽  
Data Driven

A Data-Driven Iterative Decoupling Feedforward Control Strategy With Application to an Ultraprecision Motion Stage

2015 ◽  
Vol 62 (1) ◽  
pp. 620-627 ◽  
Author(s):  
Yi Jiang ◽  
Yu Zhu ◽  
Kaiming Yang ◽  
Chuxiong Hu ◽  
Dongdong Yu
Keyword(s):  
Control Strategy ◽  
Feedforward Control ◽  
Data Driven ◽  
Motion Stage

scholarly journals Subtyping psychological distress in the population: a semi-parametric network approach

2019 ◽  
Vol 29 ◽  
Author(s):  
S. de Vos ◽  
S. Patten ◽  
E. C. Wit ◽  
E. H. Bos ◽  
K. J. Wardenaar ◽  
...  
Keyword(s):  
Psychological Distress ◽  
Community Sample ◽  
A Priori ◽  
Network Models ◽  
Optimal Choice ◽  
Data Driven ◽  
Network Density ◽  
Parametric Network ◽  
Distress Scale ◽  
Non Parametric

Abstract Aims The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and/or anxiety symptomatology. In order to do this without a priori assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach. Methods A large community sample from the Canadian general population (N = 254 443) was divided into data-driven clusters using non-parametric k-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared. Results A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness). Conclusion Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.

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