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  2. State-space representation - Wikipedia

    en.wikipedia.org/wiki/State-space_representation

    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.

  3. State space (computer science) - Wikipedia

    en.wikipedia.org/wiki/State_space_(computer_science)

    If the size of the state space is finite, calculating the size of the state space is a combinatorial problem. [4] For example, in the Eight queens puzzle, the state space can be calculated by counting all possible ways to place 8 pieces on an 8x8 chessboard. This is the same as choosing 8 positions without replacement from a set of 64, or

  4. State space search - Wikipedia

    en.wikipedia.org/wiki/State_space_search

    State space search is a process used in the field of computer science, including artificial intelligence (AI), in which successive configurations or states of an instance are considered, with the intention of finding a goal state with the desired property. Problems are often modelled as a state space, a set of states that a problem

  5. Multidimensional system - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_system

    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 ...

  6. Mamba (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Mamba_(deep_learning...

    To enable handling long data sequences, Mamba incorporates the Structured State Space sequence model (S4). [2] S4 can effectively and efficiently model long dependencies by combining continuous-time, recurrent, and convolutional models. These enable it to handle irregularly sampled data, unbounded context, and remain computationally efficient ...

  7. Subspace identification method - Wikipedia

    en.wikipedia.org/wiki/Subspace_identification_method

    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 ...

  8. Takens's theorem - Wikipedia

    en.wikipedia.org/wiki/Takens's_theorem

    The state space of the dynamical system is a ν-dimensional manifold M. The dynamics is given by a smooth map:. Assume that the dynamics f has a strange attractor with box counting dimension d A. Using ideas from Whitney's embedding theorem, A can be embedded in k-dimensional Euclidean space with

  9. State space planning - Wikipedia

    en.wikipedia.org/wiki/State_space_planning

    In artificial intelligence and computer programming, state space planning is a process used in designing programs to search for data or solutions to problems. In a computer algorithm that searches a data structure for a piece of data, for example a program that looks up a word in a computer dictionary, the state space is a collective term for all the data to be searched.