Abstract: In order to configure the Radial Basis Function(RBF) neural network model, an automatic searching algorithm based on Particle Swarm Optimization(PSO) is proposed with the thesis of phase space reconstruction. This algorithm is compared with other similar algorithms with respect to Logistic mapping and underwater acoustic signal. Experimental results show this algorithm has better performance in terms of training accuracy and convergence rate, and also supports the modeling, prediction and dynamic analysis of underwater acoustic signals.

Key words: chaotic time series, phase space reconstruction, Radial Basis Function(RBF) neural network, underwater acoustic signal

摘要： 为构建径向基函数神经网络模型，以相空间重构理论为基础，提出基于粒子群的自动搜索算法，并以Logistic映射和水声信号作为研究对象，把该算法与同类算法进行比较。实验结果表明，该算法在训练准确率和收敛速度方面体现出一定的优越性，能够为水声信号的建模、预测以及动力学分析提供支持。

关键词: 混沌时间序列, 相空间重构, 径向基函数神经网络, 水声信号

CLC Number: 

• TP393.02