brainpy.algorithms.online.RLS#

class brainpy.algorithms.online.RLS(alpha=0.1, name=None)[source]#

The recursive least squares (RLS) algorithm.

RLS is an adaptive filter algorithm that recursively finds the coefficients that minimize a weighted linear least squares cost function relating to the input signals. This approach is in contrast to other algorithms such as the least mean squares (LMS) that aim to reduce the mean square error.

See also

LMS, ForceLearning

Parameters
  • alpha (float) – The learning rate.

  • name (str) – The algorithm name.

__init__(alpha=0.1, name=None)[source]#

Methods

__init__([alpha, name])

call(identifier, target, input, output)

The training procedure.

initialize(identifier, feature_in[, feature_out])

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

register_implicit_nodes(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

save_states(filename[, variables])

Save the model states.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

unique_name([name, type_])

Get the unique name for this object.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

name

Name of the model.

postfix