lmlib.statespace#
Package Abstract :: This module provides methods to define autonomous linear state space models (ALSSMs) and to define squared error cost functions based on such ALSSMs. ALSSMs are input-free linear state space models (LSSMs), i.e., the model outputs are fully defined by a single state vector, often denoted as initial state vector x. The output vector of such a deterministic model forms the signal model used. Cost functions based on ALSSM are internally efficiently computed using recursive computation rules.
This module implements the methods published in
[Wildhaber2018] PDF,
with extensions from [Zalmai2017] and [Wildhaber2019].
Modules#
This module provides methods to define linear state space models and methods to use them as signal models in recursive least squares problems. |
|
Definition of recursively computed squared error cost functions (such as Cost Segments and Composite Costs), all based on ALSSMs |
|
Selection tool to switch between Python interpreter (default) and JIT (Just-in-Time) compilation execution for time-critical routines in package |
|
Provides useful and specific applications based on |