Study on weak signal detection in chaotic background based on prediction of GRNN

摘要：

As a new radial basis neural network, GRNN is easier to train and has better ability of function approach. In this paper, based on the characteristics of GRNN, signals in various chaotic and noisy background are predicted. Simulation results prove that GRNN has the characteristics of fast learning speed, simple design and stable structure, and the trained GRNN can implement prediction and recomposition of chaotic time series because its better ability of function approach and higher predicting precision.

关键词： Generalization regression neural network (GRNN) chaotic time series prediction

作者: SUN Qing SUN Yong TAO Jianfeng Zhu Lili

作者单位: Missile Institute, Air Force Engineering University, Sanyuan, Shaanxi, China, 713800

会议类型: 国际会议

会议名称: 第八届国际测试技术研讨会(8th International Symposium on Test and Measurement)

会议地点: 重庆

会议语种:英文

页码: 1607-1610

在线出版日期: 2009-08-01（万方平台首次上网日期，不代表论文的发表时间）