The influence of R. E. Kalman—state space theory, realization, and sampled-data systems

2019 ◽  
Vol 47 ◽  
pp. 1-6
Author(s):  
Yutaka Yamamoto
Keyword(s):  
State Space ◽  
Space Theory ◽  
Data Systems ◽  
Sampled Data ◽  
State Space Theory

scholarly journals Linear Stochastic State Space Theory in the White Noise Space Setting

2010 ◽  
Vol 48 (8) ◽  
pp. 5009-5027 ◽  
Author(s):  
Daniel Alpay ◽  
David Levanony ◽  
Ariel Pinhas
Keyword(s):  
White Noise ◽  
State Space ◽  
Space Theory ◽  
Space Setting ◽  
State Space Theory ◽  
White Noise Space

A state-space formulation of the causal linear prefiltering problem in sampled-data systems

1978 ◽  
Vol 14 (1) ◽  
pp. 75-87
Author(s):  
Yern Yeh ◽  
Craig K. Rushforth ◽  
Jacob Tal
Keyword(s):  
State Space ◽  
Data Systems ◽  
Sampled Data ◽  
Space Formulation

A State Recognition Method Based on the State Space Theory

2014 ◽  
Vol 962-965 ◽  
pp. 2778-2782
Author(s):  
Xi He ◽  
Yu Jiong Gu ◽  
Ting Xu
Keyword(s):  
State Space ◽  
Industrial Process ◽  
Space Theory ◽  
Sample Analysis ◽  
Recognition Method ◽  
State Spaces ◽  
State Recognition ◽  
Identification Process ◽  
Mechanical Devices ◽  
State Space Theory

With the development of the monitoring technology in industrial process, it is increasingly important for state recognition of the mechanical devices. By learning from the immune identification process, state space theory is used for state recognition in this paper. In this method, some state spaces are compared by the space affinity which is regarded as the calculated difference between spaces. Take the minimum space affinity as the final recognition index and then its corresponding remark in remark set can be selected. Through the sample analysis, state space theory is proved to be simple, effective and practical, and can improve the accuracy of recognition according to detailed state standard.

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