Application of artificial neural networks (ANN) on system simulation for the payload of communications satellite

Wen Li; Litian Li; Wei Zhang

doi:10.1117/12.169955

2 March 1994 Application of artificial neural networks (ANN) on system simulation for the payload of communications satellite

Wen Li, Litian Li, Wei Zhang

Author Affiliations +

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169955
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

This paper presents our continuing work in the area of nonlinear system analysis and simulation applying ANN methods. The system simulation for the payload of communications satellite is the process of predicting the overall system performance from the performance of each component in the system. The important part of this system simulation is the analysis and simulation of nonlinearity in the system, such as power transfer characteristics, and intermodulation. The model and simulation of nonlinearity for a communications satellite channel contained nonlinear components is described in this paper. Polynomial approximation by least squares and artificial neural networks simulation have been used to approximate the tested data curve of a single tone power transfer characteristics for each nonlinear element in the system. These methods have been applied to the Communications Satellite System Simulation Software Package that is developed by us in the Xi'an Institute of Space Radio Technology. Both of these methods have also been compared with the test data of real system of DFH-3 communications satellite. The results show that the approximation method using ANN concept presented in this article is more accurate and of theoretical importance and practical value in the analysis of nonlinear system problem.

Citation Download Citation

Wen Li, Litian Li, and Wei Zhang "Application of artificial neural networks (ANN) on system simulation for the payload of communications satellite", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169955

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