一种基于神经网络分形模型的一维信号表示方法
USING ITERATED FUNCTION SYSTEM BASED ON NEURAL NETWORK TO MODEL TIME SEQUENCES
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摘要: 本文提出了一种基于神经网络的分形模型,讨论了映射收缩条件,并对湖底回波进行了实验。实验结果表明,在此基础上求解分形逆问题,得到的吸引子能很好地逼近指定信号。
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关键词:
- 分形; 分形逆问题; 神经网络; 非线性
Abstract: A new method to resolve fractal inverse problem based on neural network was presented in this paper which can be employed to model a time sequences.The precondition to assure the model was also provided. A piece of echo from a lake was taken to test the algorithm. The result is satisfying. -
Barnsley M F, Demko S. Iterated function system and the global construction of fractals. Proc. Roy. Soc. London, 1985, A-399(1817): 243-275.[2] Mazel D S, Hayes M H. Using iterated function system to model discrete sequences. IEEE Trans. on SP, 1992, SP-40(7): 1724-1734.[2]赵耀,袁保宗. 一种基于新仿射变换的分形序列图像编码方法. 电子学报,1997, 25(7): 28-31.[3]谢和平, 薛秀谦. 分形应用中的数学基础与方法. 北京: 科学出版社,1997, 27-40, 99-106, 170-175.[4]Hecht-Nielsen R. Theory of the backpropagation neural network. IJCNN, San Diego: 1989, 1, 593-605.[5]罗发龙, 李衍达. 神经网络信号处理. 北京: 电子工业出版社, 1993, 7-7. -
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