The success of neural network architectures depends heavily on the  availability
of effective learning  algorithms. Radial basis  function (RBF) neural  networks
provide  attractive  possibilities  for solving  signal  processing  and pattern
classification  problems. Gradient  descent training  (GD) of  RBF networks  has
proven  to  be much  more  effective than  more  conventional methods.  However,
gradient  descent training  can be  computationally expensive  and its  learning
speed is  very slow.  This paper  compares (GD)  to the  method based  on either
Kalman filtering (KF) or decoupled Kalman filter (DEKF). These new methods prove
to  be  quicker  than  gradient  descent  training  while  still  providing good
performance at the same level of  effectiveness as they are used in  fingerprint
-based positioning.