关键词: 非线性系统辨识, 多项式模型, 一体化辨识, 递归改进Gram-Schmidt(RMGS)算法, 改进正交化次序

Abstract: An integration algorithm of nonlinear polynomial model structure identification and parameter identification was proposed for the linear parametric polynomial assembled model, which had wider significance in the field of nonlinear systems. The algorithm combined optimal-selecting process based on contribution items with poor-eliminating process based on redundant items in structure identification. In the optimal-selecting process, the recursive modified Gram-Schmidt (RMGS) algorithm based on output vector residual was used to select the better terms in the vector space, and some redundant non-model terms were allowed to be selected, according to the maximizing drop of the output vector projection residual. In the poor-eliminating process, the algorithm adopted the model structure poor-eliminating strategy based on modified orthogonal sequence to deal with the contribution of the orthogonal vector equally. The structure items with small contribution to the actual output were deleted from the optimal set. The structure and parameters were determined by the system completeness index. Two examples of typical nonlinear polynomial model identification simulation demonstrate the effectiveness of the algorithm.

Key words: nonlinear system identification, polynomial model, integration identification, recursive modified Gram-Schmidt (RMGS) algorithm, modified orthogonal sequence