Gupta Kopal, Singh Harinder P, Biswal B, Ramaswamy R
Department of Physics and Astrophysics, University of Delhi, Delhi 110007, India.
Chaos. 2006 Jun;16(2):023116. doi: 10.1063/1.2204749.
We demonstrate a technique for the enhancement of chaos in a computational model of a periodically stimulated excitable neuron. "Anticontrol" of chaos is achieved through intermittent adaptive intervention, which is based on finite-time Lyapunov exponents measured from the time series. Our results suggest that an adaptive strategy for chaos anticontrol is viable for increasing the complexity in physiological systems that are typically both noisy and nonstationary.
我们展示了一种在周期性刺激的可兴奋神经元计算模型中增强混沌的技术。通过基于从时间序列测量的有限时间李雅普诺夫指数的间歇性自适应干预实现了混沌的“反控制”。我们的结果表明，混沌反控制的自适应策略对于增加通常既嘈杂又非平稳的生理系统的复杂性是可行的。
