HomeokinBase Class Reference

Abstract class (interface) for robot controller that use are based on the homeokinetic prinziple. More...

#include <homeokinbase.h>

Inherits AbstractController.

Inherited by InvertMotorController.

Inheritance diagram for HomeokinBase:

[legend]Collaboration diagram for HomeokinBase:

[legend]List of all members.

Public Member Functions
	HomeokinBase (unsigned short buffersize, const std::string &name, const std::string &revision)
Protected Member Functions
void	putInBuffer (matrix::Matrix *buffer, const matrix::Matrix &vec, int delay=0)
	put new value in ring buffer
virtual matrix::Matrix	calculateDelayedValues (const matrix::Matrix *buffer, int number_steps_of_delay_)
	calculate delayed values
virtual matrix::Matrix	calculateSmoothValues (const matrix::Matrix *buffer, int number_steps_for_averaging_)
	calculate time-smoothed values
virtual double	calcErrorFactor (const matrix::Matrix &e, bool loga, bool root)
	calculates the error_factor for either logarithmic (E=ln(e^T*e)) or square (E=sqrt(e^t*e)) error
Static Protected Member Functions
static double	g (double z)
	neuron transfer function
Protected Attributes
paramval	epsC
	learning rate factor for controller learning
paramval	epsA
	learning rate factor for model learning
paramval	factorB
	additional learning rate factor for model bias
paramint	s4delay
	number of timesteps of delay in the SML
paramint	s4avg
	number of timesteps used for smoothing the controller output values
paramint	logaE
	logarithmic error is used for learning 1: controller 2: model 3: both
paramint	rootE
	root error is used for learning 1: controller 2: model 3: both
paramval	squashSize
	size of the box, where the parameter updates are clipped to
int	t
unsigned short	buffersize
bool	initialised

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Detailed Description

Abstract class (interface) for robot controller that use are based on the homeokinetic prinziple.

Implements standard buffers and configureable interface for some useful parameters like epsC, epsA, s4avg ...

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Constructor & Destructor Documentation

HomeokinBase	(	unsigned short	buffersize,
		const std::string &	name,
		const std::string &	revision
	)			[inline]

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Member Function Documentation

virtual double calcErrorFactor	(	const matrix::Matrix &	e,
		bool	loga,
		bool	root
	)			[inline, protected, virtual]

calculates the error_factor for either logarithmic (E=ln(e^T*e)) or square (E=sqrt(e^t*e)) error

virtual matrix::Matrix calculateDelayedValues	(	const matrix::Matrix *	buffer,
		int	number_steps_of_delay_
	)			[inline, protected, virtual]

calculate delayed values

virtual matrix::Matrix calculateSmoothValues	(	const matrix::Matrix *	buffer,
		int	number_steps_for_averaging_
	)			[inline, protected, virtual]

calculate time-smoothed values

static double g

(

double

z

)

[inline, static, protected]

neuron transfer function

void putInBuffer	(	matrix::Matrix *	buffer,
		const matrix::Matrix &	vec,
		int	delay = 0
	)			[inline, protected]

put new value in ring buffer

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Member Data Documentation

unsigned short buffersize [protected]

paramval epsA [protected]

learning rate factor for model learning

paramval epsC [protected]

learning rate factor for controller learning

paramval factorB [protected]

additional learning rate factor for model bias

bool initialised [protected]

paramint logaE [protected]

logarithmic error is used for learning 1: controller 2: model 3: both

paramint rootE [protected]

root error is used for learning 1: controller 2: model 3: both

paramint s4avg [protected]

number of timesteps used for smoothing the controller output values

paramint s4delay [protected]

number of timesteps of delay in the SML

paramval squashSize [protected]

size of the box, where the parameter updates are clipped to

int t [protected]

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The documentation for this class was generated from the following file:

• homeokinbase.h

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