lds::poisson::System #

Poisson System type.
#include <lds_poisson_sys.h>

Public Functions #

	Name
	System() =default Constructs a new System.
	System(std::size_t n_u, std::size_t n_x, std::size_t n_y, data_t dt, data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0) Constructs a new Poisson System.
virtual const Vector &	Simulate(const Vector & u_tm1) override Simulate system measurement.

Protected Functions #

	Name
virtual void	h() override System output function.
virtual void	RecurseKe() override Recursively recalculate estimator gain (Ke)

Additional inherited members #

Public Functions inherited from lds::System

	Name
virtual	~System()
void	Filter(const Vector & u_tm1, const Vector & z) Filter data to produce causal state estimates.
void	f(const Vector & u, bool do_add_noise =false) system dynamics function
size_t	n_u() const Get number of inputs.
size_t	n_x() const Get number of states.
size_t	n_y() const Get number of outputs.
data_t	dt() const Get sample period.
const Vector &	x() const Get current state.
const Matrix &	P() const Get covariance of state estimate.
const Vector &	m() const Get current process disturbance/bias.
const Matrix &	P_m() const Get covariance of process disturbance estimate.
const Vector &	cx() const Get C*x.
const Vector &	y() const Get output.
const Vector &	x0() const Get initial state.
const Vector &	m0() const Get initial disturbance.
const Matrix &	A() const Get state matrix.
const Matrix &	B() const Get input matrix.
const Vector &	g() const Get input gain/conversion factor.
const Matrix &	C() const Get output matrix.
const Vector &	d() const Get output bias.
const Matrix &	Ke() const Get estimator gain.
const Matrix &	Ke_m() const Get estimator gain for process disturbance (m)
const Matrix &	Q() Get process noise covariance.
const Matrix &	Q_m() Get process noise covariance of disturbance evoluation.
const Matrix &	P0() Get covariance of initial state.
const Matrix &	P0_m() Get covariance of initial process disturbance.
void	set_A(const Matrix & A) Set state matrix.
void	set_B(const Matrix & B) Set input matrix.
void	set_m(const Vector & m, bool do_force_assign =false) Set process disturbance.
void	set_g(const Vector & g) Set input gain.
void	set_Q(const Matrix & Q) Set process noise covariance.
void	set_Q_m(const Matrix & Q_m) Set process noise covariance of disturbance evoluation.
void	set_x0(const Vector & x0) Set initial state.
void	set_P0(const Matrix & P0) Set covariance of initial state.
void	set_P0_m(const Matrix & P0_m) Set covariance of initial process disturbance.
void	set_C(const Matrix & C) Set output matrix.
void	set_d(const Vector & d) Set output bias.
void	set_x(const Vector & x) Set state of system.
void	Reset() Reset system variables.
void	Print() Print system variables to stdout.

Protected Functions inherited from lds::System

	Name
void	InitVars(data_t p0 =kDefaultP0, data_t q0 =kDefaultQ0)

Public Attributes inherited from lds::System

	Name
bool	do_adapt_m whether to adaptively estimate disturbance m

Protected Attributes inherited from lds::System

	Name
std::size_t	n_x_ number of states
std::size_t	n_u_ number of inputs
std::size_t	n_y_ number of outputs
data_t	dt_ sample period
Vector	x_ state
Matrix	P_ covariance of state estimate
Vector	m_ process disturbance
Matrix	P_m_ covariance of disturbance estimate
Vector	cx_ C*x.
Vector	y_ output
Vector	z_ measurement
Vector	x0_ initial state
Matrix	P0_ covariance of initial state estimate
Vector	m0_ initial process disturbance
Matrix	P0_m_ covariance of initial disturbance est.
Matrix	A_ state matrix
Matrix	B_ input matrix
Vector	g_ input gain
Matrix	Q_ covariance of process noise
Matrix	Q_m_ covariance of disturbance random walk
Matrix	C_ output matrix
Vector	d_ output bias
Matrix	Ke_ estimator gain
Matrix	Ke_m_ estimator gain for process disturbance

Public Function Details #

System #

System() =default

System #

System(
    std::size_t n_u,
    std::size_t n_x,
    std::size_t n_y,
    data_t dt,
    data_t p0 =kDefaultP0,
    data_t q0 =kDefaultQ0
)

Parameters:

n_u number of inputs
n_x number of states
n_y number of outputs
dt sample period
p0 [optional] initial diagonal elements of state estimate covariance (P)
q0 [optional] initial diagonal elements of process noise covariance (Q)

Simulate #

virtual const Vector & Simulate(
    const Vector & u_tm1
) override

Parameters:

u_tm1 input at t-1

Return: z measurement

Reimplements: lds::System::Simulate

Simulate system and produce measurement

Protected Function Details #

h #

inline virtual void h() override

Reimplements: lds::System::h

RecurseKe #

virtual void RecurseKe() override

Reimplements: lds::System::RecurseKe

Recursively recalculate estimator gain (Ke).

References:

Smith AC, Brown EN. (2003) Estimating a State-Space Model from Point Process Observations. Neural Computation 15.

Eden UT, …, Brown EN. (2004) Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering Neural Computation 16.

Updated on 19 May 2022 at 17:16:04 Eastern Daylight Time