11318 modules
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ELEC6262 2027-28
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC3228 2027-28
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC6262 2025-26
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC6262 2026-27
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC3228 2029-30
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC6262 2030-31
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC3228 2028-29
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
ELEC6262 2029-30
From Data to Dynamical Model: System Identification
The main problem in system identification is deriving mathematical models of dynamical systems (for transfer function, state-space) from data. Such problem arises for example in control, when the complexity of a model or lack of physical insight prevent the development of a model from first principles to be used in designing a controller.
The algorithms to solve the system identification problem rely, for the linear case, on mathematical methodologies formalised with (relatively) simple linear algebra.
The objective of this course is to give a broad but non superficial introduction to some of the main themes in system identification of discrete-time systems: the use of Hankel matrices and of regression and algebraic structures based on time shifts to derive transfer-function and state-space models. -
LING1002 2025-26
From English to Englishes
This module introduces you to the spread of English from its historical origins to colonial and postcolonial contexts and, further, to current global contexts. It explores sociolinguistic issues and debates centering on the development of English. It engages with different approaches to the understanding of English - for example, World Englishes, Global Englishes and English as a lingua franca - in local and global contexts. -
LING1002 2027-28
From English to Englishes
This module introduces you to the spread of English from its historical origins to colonial and postcolonial contexts and, further, to current global contexts. It explores sociolinguistic issues and debates centering on the development of English. It engages with different approaches to the understanding of English - for example, World Englishes, Global Englishes and English as a lingua franca - in local and global contexts.