基于SPPSO算法的时滞HBV模型的系统辨识研究

Research on System Identification by SPPSO Programming for Time-delay HBV Model

系统辨识是现代控制理论中的一个很活跃的分支。目前的系统辨识多采用二次规划等解析算法,不足之处在于可辨识的参数少、收敛慢、对参数的初值依赖大。随着智能控制领域研究的不断发展,非线性程度也就越来越高,一些经典的方法很难满足需要。而小种群粒子群算法(SPPSO)作为一种全局优化算法,易于实现,且收敛速度快,计算效率高,在处理数据量较大的大规模种群问题时可大大降低时间和资源的开销,因此在系统辨识特别是高度非线性、时滞系统中更具有意义。而这类复杂的系统在医学系统中具有典型性。所以将该算法用于求解时滞的乙型肝炎动力学模型有很好的研究价值和实用价值。

Systems identification is an active research area of intelligent control theory. Existing algorithms like quadra- tic programming method can identify very limited parameter＇s number, has the limitations of stagnation and heavily de pendent on initial values of the parameters. With the continuous development of the area of intelligent control, the de gree of nonlinearity becomes higher and higher. But the method of nonlinear system identification has not formed a com- plete scientific theory system. Small population-based particle swarm optimization （SPPSO） is an optimization technique for locating the global optimum. SPPSO is easy to realize, quick convergence and effective. It can greatly reduce the time and resource costs in the processing of large data quantity of large-scale population problem. So, in system identifica tion, especially in highly nonlinear and time-delay system it is more meaningful, and this kind of complex system is typi- cal in medical system. SPPSO is used in solving time-delay hepatitis B virus dynamics （HBV） model. It has good re- search and practical value.