Input reconstruction of chaos sensors.
Although the sensitivity of sensors can be significantly enhanced using chaotic dynamics due to its extremely sensitive dependence on initial conditions and parameters, how to reconstruct the measured signal from the distorted sensor response becomes challenging. In this paper we suggest an effective method to reconstruct the measured signal from the distorted (chaotic) response of chaos sensors. This measurement signal reconstruction method applies the neural network techniques for system structure identification and therefore does not require the precise information of the sensor's dynamics. We discuss also how to improve the robustness of reconstruction. Some examples are presented to illustrate the measurement signal reconstruction method suggested.
Yu
Dongchuan
D
College of Automation Engineering, Qingdao University, Qingdao, Shandong 266071, People's Republic of China. dongchuanyu@yahoo.com
Liu
Fang
F
Lai
Pik-Yin
PY
D000465:Algorithms; D003198:Computer Simulation; D004867:Equipment Design; D005246:Feedback; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D013598:Systems Theory