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

(c) 1 (d)
Figure 4: The behavior of squared error function.
In this paper we consider that the communication control in
mutual connected network by reproductive and competitive
radial basis function network. We propose RC-RBFN by
applying the synaptic plasticity equation as the survival of the
fittest learning to the RBFN. The CRBFN, which we first
propose can faster learning by using the survived minimum,
required input neurons. From the results of simulation, it is
shown that the RC-RBFN can estimate the network
parameters by using optimum number of neurons after
eliminating the redundant neurons.
The further problem is that we exponent the synaptic
plasticity equation as survival of the fittest learning algorithm
to apply other network problems.
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