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Research on Modeling and Application of Equipment System Based on RBF Neural Networks

Author: MengHuiZuo
Tutor: WangJinDong；WuXiaoYue
School: PLA Information Engineering University
Course: Military Equipment
Keywords: Equipment system Satellite earth stations Element Modeling RBFNN Training samples ROLS-GA
CLC: E911
Type: Master's thesis
Year: 2008
Downloads: 48
Quote: 1
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Abstract


Modern high-tech conditions, the equipment system expanding, more complex functions , the relationship between the system more closely for the sharp increase in the cost of system construction , which are made ??on the equipment system demonstration urgent needs . The equipment system for modeling and analysis equipment system demonstrated the key . Current equipment system modeling method has made great development, but there are many who argue for the system imperfections. Among the many equipment system modeling approach, simulation element modeling ideological arguments made ??specifically for the system , with a good theoretical basis, but effective equipment system element modeling is not much. In this paper, based on RBFNN equipment system modeling method of modeling techniques and make some key examples of verification, with good RBFNN model function approximation ability and agility , improve system demonstration model of the equipment support capabilities . The main work and innovation articles include the following aspects : 1 ) proposed equipment system based on RBFNN exploratory modeling framework , the framework 's core role is to describe the modeling process and the need to solve technical problems, to provide guidance for the full text of the study . 2 ) analysis of the existing meta-model evaluation criteria for existing standards do not reflect the needs of users have an impact on the results of the evaluation model flaws that reflect the needs of users of the equipment system meta-model evaluation criteria . On this basis, the equipment system design needs priority training samples RBFNN model construction algorithm , and finally a numerical example and comparative analysis . 3 ) In the process of introducing training ROLS GA algorithm, GA optimization RBF center width , to achieve full RBFNN structural optimization , and finally a numerical example and comparative analysis . 4 ) to satellite earth station resource optimization as a background study conducted RBFNN modeling applications , using the above method proposed to solve the model structure and RBFNN training sample structure optimization are two key issues . This study enriched and developed the equipment system modeling approach for the promotion of domestic equipment system demonstration based modeling technology, and support for our military faces satellite earth station systems and other complex equipment system has a very important demonstration of the theoretical and practical significance .

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