A closed-loop identification protocol for nonlinear dynamical systems
A closed-loop identification protocol for nonlinear dynamical systems
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DOI：
10.1021/jp056189o
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发表时间：
2006-06-29
影响因子：
2.9
通讯作者：
Le Bris, Claude
中科院分区：
文献类型：
--
作者：
Feng, Xiao-jiang;Rabitz, Herschel;Le Bris, Claude
以前的工作介绍了一个最佳识别（OI）技术可靠地提取模型参数的生化反应系统定制的实验室实验。最优性的概念是通过在实验室中寻找一个外部控制来产生数据，从而使所确定的参数分布的不确定性最小。本文对OI进行了一些算法和操作上的改进，旨在为非线性动态系统建立一个更实用、更高效的闭环辨识协议/过程（CLIP）。CLIP中的改进包括（a）反演成本函数修改以优选地搜索与观测数据一致的参数分布的上边界和下边界，（B）未知参数的动态搜索范围更新以更好地利用来自先前迭代实验的信息，（c）用单纯形法代替控制遗传算法，以实现操作成本和反演质量之间的更好平衡，以及（d）利用虚拟灵敏度优化技术来进一步降低实验室成本。CLIP利用这些新算法的工作原理说明在识别一个模拟的tRNA校对模型，结果表明增强性能的CLIP算法的可靠性和效率。
A previous work introduced an optimal identification (OI) technique for reliably extracting model parameters of biochemical reaction systems from tailored laboratory experiments. The notion of optimality enters through seeking an external control in the laboratory producing data that leads to minimum uncertainties in the identified parameter distributions. A number of algorithmic and operational improvements are introduced in this paper to OI, aiming to build a more practical and efficient closed-loop identification protocol/procedure (CLIP) for nonlinear dynamical systems. The improvements in CLIP include (a) inversion cost function modification to preferably search for the upper and lower boundaries of the parameter distributions consistent with the observed data, (b) dynamic search range updating of the unknown parameters to better exploit the information from the prior iterative experiments, (c) replacing the control genetic algorithm by the simplex method to enable better balance between operational cost and inversion quality, and (d) utilizing virtual sensitivity optimization techniques to further reduce the laboratory costs. The workings of CLIP utilizing these new algorithms are illustrated in indentifying a simulated tRNA proofreading model, and the results demonstrate enhanced performance of CLIP in terms of algorithmic reliability and efficiency.