Sharing and cooperation in geo-information technology

Aziz, T. Lukman

Bibliographic data

Monograph

Persistent identifier:: 856479470

Author:: Aziz, T. Lukman

Title:: Sharing and cooperation in geo-information technology

Sub title:: ISPRS Commission VI Symposium, April 15 - 17, 1999, Bandung, Indonesia

Scope:: 1 Online-Ressource (130 Seiten)

Year of publication:: 1999

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856479470

Illustration:: Illustrationen, Diagramme

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: SPECIAL SESSION: EARTH MONITORING

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: THE COMMUNICATION CONTROL IN MUTUAL CONNECTED NETWORK BY RC-RBFN. Koji Okuhara, Haruhiro Fujita and Toshijiro Tanaka

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 6. Bandung-lndonesia 1999 116 THE COMMUNICATION CONTROL IN MUTUAL CONNECTED NETWORK BY RC-RBFN 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 ABSTRACT 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. I. INTRODUCTION 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 remarks. 2. THE OUTLINE OF CRBFN 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.

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