基于进化ANFIS的短波通信频率参数预测
Prediction of Frequency Parameters of Short-Wave Communication Based on ANFIS Evolved
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摘要: 该文提出并设计了一种利用神经模糊推理系统建模的短波通信频率参数预测模型。该模型以模糊系统为平台，利用自学习算法训练建立推理规则，采用并行自适应遗传算法进化调整系统内部参数。通过ff0F2实测数据仿真试验，并与神经网络方法、混沌和神经网络相结合方法进行比较，结果证明该模型具有预测精度高、收敛速度快、全局收敛性好、内部参数调整智能化等突出优点。Abstract: This paper presents a prediction model of frequency parameters of short-wave communication based on Adaptive Neural Fuzzy Inference System(ANFIS). The system parameters of the model are adjusted by delaminating-adaptation genetic algorithm. The model is simulated and compared with other nonlinear methods. The model shows some standout excellences, such as higher precision of prediction, faster speed of convergence, better across-the-board astringency and intelligence of adjusting system parameters.
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简相超，郑君里. 混沌和神经网络相结合预测短波通信频率参数[J]. 清华大学学报，2001，41(1): 16－19.[2]曾文，张训械等. 利用人工神经网络建立我国南海地区电离层模式[J]. 地球物理学报，1999，45(5): 582－589.[3]Takens F. Detecting strange attractors in turbulence [A]. Dynamical Systems and Turbulence, Lecture Notes in Mathematics[C]. Berlin: Springer-Verlag,1981, Vol.898: 366－ 381.[4]Casdagli M. Nonlinear prediction of chaotic time series [J].Physica D.1989, 35:335-356[5]Takens F. On the numerical determination of the dimension of an attractor [A]. Dynamical Systems and Turbulence, Lecture Notes in Mathematics[C]. Berlin: Springer-Verlag, 1981, Vol.898: 230－241.[6]Abarbanel D I. Analysis of Observed Chaotic Data [M]. NewYork: Springer-Verlag,1996:75－82.[7]Zadeh L A. Fuzzy Sets [J].Information and Control.1965, 8:338-353[8]Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutations in gas [J]. IEEE Trans. on SMC, 1994, 24(4):656－667.[9]阎平凡等. 人工神经网络与模拟进化计算[M]. 北京:清华大学出版社, 2000: 301－302.王小平等. 遗传算法[M]. 西安:西安交通大学出版社, 2000: 28 －38.[10]陈国良等. 遗传算法及应用[M]. 北京:人民邮电出版社,1996:75－88. -
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