基于视神经网络的混沌时间序列奇异信号实时检测算法

提出了一种基于视神经网络的实时检测混沌时间序列中的奇异点算法,设计了视神经网络奇异点检测器(RNNND);然后设计了基于反向传播(BP)神经网络和径向基函数(RBF)神经网络的混沌时间序列奇异点检测器.利用Lorenz理论模型产生的时间序列和实测输油管道压力时间序列分别检验了这3个奇异点检测器在抗干扰能力、检测微弱信号能力和运算速度等方面的性能.仿真和分析表明,RNNND具有良好的检测精度和较快检测速度.最后详细分析了3种奇异点检测器优缺点并给出了适用场合.

Real-time novelty detector of chaotic time series based on retina neural network

A kind of novelty detection method based on retina neural network is proposed, which could find the novelty in chaotic time series. To demonstrate the capability of the novelty detection method, we designed three novelty detectors,namely the neural network novelty detector (RNNND), back-propagation(BP) novelty detector (BPND) and radial base function(RBF) novelty detector (RBFND), which are based on retina neural network, BP neural network and RBF neural network, respectively. Using Lorenz time series and oil pipeline pressure time series, we tested the performance of the three novelty detectors, including performances of anti-jamming, micro-novelty detection and the computing speed. The results show that the three novelty detectors have good precision and fast computing speed. Finally, the merits and shortcomings of the proposed novelty detection method are analyzed based on retina neural network, BP and RBF neural network, and their applicabilities are given.