Open AccessFull-order and reduced-order optimal synchronization of neurons model with unknown parameters
Author(s) -
Xingyuan Wang,
Xiaoyong Ren,
Yong-Lei Zhang
Publication year - 2012
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.61.060508
Subject(s) - synchronizing , control theory (sociology) , synchronization (alternating current) , controller (irrigation) , computer science , nonlinear system , optimal control , process (computing) , trajectory , lyapunov stability , lyapunov function , stability (learning theory) , biological neuron model , mathematical optimization , control (management) , artificial neural network , mathematics , artificial intelligence , physics , telecommunications , channel (broadcasting) , computer network , quantum mechanics , transmission (telecommunications) , astronomy , machine learning , agronomy , biology , operating system
Based on Lyapunov stability theory, optimal control principle and step design methodology, nonlinear feedback controller and optimal controller are designed, in which the nonlinear feedback controller makes the trajectory error between two neuron systems tend to zero, and the optimal controller makes the spent energy meet minimum, which is spent in the process of synchronizing. In this paper, the uncertain cable model is taken as an example to illustrate the full-order optimal synchronization of two neurons. The uncertain cable model and the uncertain Hindmarsh-Rose (HR) model are taken to illustrate the reduced-order optimal synchronization of two neurons. In addition, the unknown parameters are identified successfully. Numerical Simulation results show the effectiveness of the strategy further.