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

International Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 6. Bandung-lndonesia 1999
Koji Okuhara*, Haruhiro Fujita** and Toshijiro Tanaka*
* Hirhoshima Prefectural University
567 Nanatuka, Shyobara, Hiroshima, Japan 727-0023
** Shikoku National Agricultural Experiment Station
Ministry of Agriculture, Forestry and Fisheries
1-3-1 Senyu, Zentsuji, Kagawa, Japan 765-8508
e-mail: okuhara@bus.hiroshima-pu.ac.jp
The mutual connected network can be controlled by the decentralized method. The network, generally speaking, has
some restriction of the resource. We must control network parameters relating to such resources. In this paper we
consider that the communication control in mutual connected network by reproductive and competitive radial basis
function network.
We first propose a competitive radial basis function network as learning algorithm. To solve the optimal problem of
network system, we further extend the learning algorithm to reproductive criteria of network parameter. Compared with
the usual radial basis function network it is shown that the reproductive competitive radial basis function network can
faster find optimal value of parameter.
Networks consist of nodes as elements, line connecting
these nodes and transition. Many kinds of neural networks
have been proposed by differences in the transition.
Information processing which deeply concerns
communications is executed in the nodes and lines. The
problem of the network is the restriction of the resources
at there. We propose the neural network algorithms to
solve such optimization problem.
Compared with multi-layer neural networks, by the way, a
radial basis function network (RBFN) has excellent
properties such as executing the local learning about each
neuron. Thus, it is applied to the function approximate
problem, the optimization problem and so on. In the
initial state, however, the RBFN require the redundant
neurons in order to approximate an uncertain nonlinear
function because no one can not know the number of
required neurons in advance. It is generally known that
increasing the number of neurons implies the delay of
learning and the over learning. To avoid this problem
many methods have been proposed. But on decreasing
dimensions of the RBFN, the criteria determining how
many neurons should be left has not been established. We
focus on this point and try to clarify the criteria. It is
desirable that the criteria is naturally derived. For this
purpose, we propose the reproductive and competitive
radial basis function network (RC-RBFN).
This paper is organized as follows. The outline of
CRBFN described in the next section. In section 3 we
propose the RC-RBFN determining the network
parameters by learning algorithm. The experimental
results appear in section4, followed by concluding
The RBFN is one of the neural networks approximating
to a nonlinear function by adding up the radial basis
function. One can consider the normalized Gaussian
active function as the radial basis function. The structure
of the RBFN with M input neurons and 1 output neuron is
drawn by Figure 1.