中文摘要：

针对多变量系统维数大、参数多、一般的辨识算法计算量大的问题,基于耦合辨识概念,推导多变量系统的耦合随机梯度算法,利用鞅收敛定理分析算法的收敛性能.算法的主思想是将系统模型分解为多个单输出子系统,在子系统的递推辨识过程中,将每个子系统的参数估计值耦合起来.所提出算法与最小二乘算法和耦合最小二乘算法相比,具有较少的计算量,收敛速度可以通过引入遗忘因子得到改善.性能分析表明了所提出算法收敛,仿真实例验证了算法的有效性.

英文摘要：

It is an issue that multivariable systems with high dimensions have many parameters, resulting in heavy computational costs in identification methods. Therefore, a coupled stochastic gradient algorithm is derived for multivariable systems based on the coupling identification concept. The identification model is decomposed into several single-output systems, and the parameter estimates are coupled during the subsystem identification by using the gradient search. The convergence properties are analyzed by using the martingale convergence theorem. Compared with the recursive least squares algorithm and the coupled least squares algorithm, the proposed algorithm has less computational load. The convergence rate can be improved by introducing a forgetting factor. Performance analysis verifies that the proposed algorithm converges.The simulation results show the effectiveness of the proposed algorithm.