System Identification and Parameter Estimation Delft University of Technology

WB2301. System Identification and Parameter Estimation. System identification is an important tool to estimate the dynamics of a system using input and output data, and to gain more insight into the system under investigation. During this course the mathematical background of system identification in both time domain and frequency domain is given, including closed-loop systems (i.e. systems under feedback). Furthermore methods will be presented to translate the identified system dynamics into physical parameters using physical models (parameter estimation). During the course examples from both technical and physiological system (i.e. estimate the behavior of a driver or the dynamics of a human joint) will be discussed.

WB2301. System Identification and Parameter Estimation. System identification is an important tool to estimate the dynamics of a system using input and output data, and to gain more insight into the system under investigation. During this course the mathematical background of system identification in both time domain and frequency domain is given, including closed-loop systems (i.e. systems under feedback). Furthermore methods will be presented to translate the identified system dynamics into physical parameters using physical models (parameter estimation). During the course examples from both technical and physiological system (i.e. estimate the behavior of a driver or the dynamics of a human joint) will be discussed.

WB2301. System Identification and Parameter Estimation. System identification is an important tool to estimate the dynamics of a system using input and output data, and to gain more insight into the system under investigation. During this course the mathematical background of system identification in both time domain and frequency domain is given, including closed-loop systems (i.e. systems under feedback). Furthermore methods will be presented to translate the identified system dynamics into physical parameters using physical models (parameter estimation). During the course examples from both technical and physiological system (i.e. estimate the behavior of a driver or the dynamics of a human joint) will be discussed.

WB2301. System Identification and Parameter Estimation. System identification is an important tool to estimate the dynamics of a system using input and output data, and to gain more insight into the system under investigation. During this course the mathematical background of system identification in both time domain and frequency domain is given, including closed-loop systems (i.e. systems under feedback). Furthermore methods will be presented to translate the identified system dynamics into physical parameters using physical models (parameter estimation). During the course examples from both technical and physiological system (i.e. estimate the behavior of a driver or the dynamics of a human joint) will be discussed.

WB2301. System Identification and Parameter Estimation. System identification is an important tool to estimate the dynamics of a system using input and output data, and to gain more insight into the system under investigation. During this course the mathematical background of system identification in both time domain and frequency domain is given, including closed-loop systems (i.e. systems under feedback). Furthermore methods will be presented to translate the identified system dynamics into physical parameters using physical models (parameter estimation). During the course examples from both technical and physiological system (i.e. estimate the behavior of a driver or the dynamics of a human joint) will be discussed.

WB2301. System Identification and Parameter Estimation. System identification is an important tool to estimate the dynamics of a system using input and output data, and to gain more insight into the system under investigation. During this course the mathematical background of system identification in both time domain and frequency domain is given, including closed-loop systems (i.e. systems under feedback). Furthermore methods will be presented to translate the identified system dynamics into physical parameters using physical models (parameter estimation). During the course examples from both technical and physiological system (i.e. estimate the behavior of a driver or the dynamics of a human joint) will be discussed.