Search results
Results From The WOW.Com Content Network
The state space or phase space is the geometric space in which the axes are the state variables. The system state can be represented as a vector , the state vector . If the dynamical system is linear, time-invariant, and finite-dimensional, then the differential and algebraic equations may be written in matrix form.
State/Space theory constitutes a new branch of social and political geography in which the issues of space as a geographic element are considered for their influence on political relationships and outcomes. [1] Leading scholars include Neil Brenner at the Harvard Graduate School of Design, and Bob Jessop at Lancaster University in England ...
State Space Model. Add languages. Add links. Article; Talk; ... Download QR code; Print/export Download as PDF; Printable version; In other projects
A model class that is general enough to capture this situation is the class of stochastic nonlinear state-space models. A state-space model is usually obtained using first principle laws, [16] such as mechanical, electrical, or thermodynamic physical laws, and the parameters to be identified usually have some physical meaning or significance. A ...
A state-space model is a representation of a system in which the effect of all "prior" input values is contained by a state vector. In the case of an m-d system, each dimension has a state vector that contains the effect of prior inputs relative to that dimension. The collection of all such dimensional state vectors at a point constitutes the ...
The Bellman filter is an algorithm that estimates the value sequence of hidden states in a state-space model.It is a generalization of the Kalman filter, allowing for nonlinearity in both the state and observation equations.
Vacuum World, a shortest path problem with a finite state space. In computer science, a state space is a discrete space representing the set of all possible configurations of a "system". [1] It is a useful abstraction for reasoning about the behavior of a given system and is widely used in the fields of artificial intelligence and game theory.
In mathematics, specifically in control theory, subspace identification (SID) aims at identifying linear time invariant (LTI) state space models from input-output data. SID does not require that the user parametrizes the system matrices before solving a parametric optimization problem and, as a consequence, SID methods do not suffer from problems related to local minima that often lead to ...