Felix Ramon A, Sanchez Edgar N, Chen Guanrong
CINVESTAV, Unidad Guadalajara, Mexico.
IEEE Trans Neural Netw. 2004 Nov;15(6):1450-7. doi: 10.1109/TNN.2004.836236.
In this paper, we present a new approach for chaos reproduction using variable structure recurrent neural networks (VSRNN). A neural network identifier is designed, with a variable structure that will change according to its output performance as compared to the given orbits of an unknown chaotic systems. A tradeoff between identification errors and computational complexity is discussed.
在本文中，我们提出了一种使用变结构递归神经网络（VSRNN）进行混沌再现的新方法。设计了一种神经网络标识符，其结构可变，会根据与未知混沌系统给定轨道相比的输出性能而改变。讨论了识别误差与计算复杂度之间的权衡。
