You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Sharing and cooperation in geo-information technology
Aziz, T. Lukman

2(a) shows both the teaching signal and the radial basis
function prepared at the initial state. Figure 2(b) shows
We show the ability of proposed method. For this the squared error function when the radial basis function
purpose, the function is generating the teaching signal. It is removed,
is supposed that one radial basis function is prepared at
the initial state, the synaptic weight is given by 1. Figure
Figure 2: The distribution of the teaching signals and the initial radial basis function.
To paying attention to the efficiency of the reproduction, the
updates for the synaptic weight and the parameter are omitted
at Step 1. That is, added radial basis function with the same
synaptic weight and the parameter is set to the parameter
when the new stable convergence is detected. This means that
such model is equivalent to RCRBFN whose parameter can
be adjusted but the synaptic weight and the parameter can not
Figure 3 shows that the change of the stable convergence of
the parameter during learning. The horizontal axis denotes
the parameter and the vertical axis the free energy, where the
constant is a coefficient for easily viewing the figure. The
stable convergence is shown by the black filled circle. F'igure
4 shows the change of the squared error function during
Figure 3: The stable fixed point of parameter m during learning.