一种T-S 模型的在线辨识算法

提出一种能通过输入输出数据在线获得T-S模型的结构和参数的辨识算法。首先，对输入空间进行划分，并在线优化子空间的形状和个数；然后，通过RLS更新子模型参数，使各个子模型逼近当前工况的实际系统；当子空间生成或形状发生变化时，调整相应子模型参数和数据矩阵；最后，针对非线性动态系统和煤气炉数据进行仿真实验，验证了所提出算法的有效性。

An on-line algorithm for T-S model identification

An on-line algorithm for T-S model identification is proposed, which automates the structure and parameter identification simultaneously based on input-target samples. Firstly, several subspaces are produced in the input space, and  their shape and distribution are optimized on-line. Then the recursive least square algorithm is employed to update the parameters of each local model, which makes each local model approximate the real system. If a new subspace is produced or the shape of the subspace is changed, an adjustment method is used for each local parameters and data matrix. Finally, a simulation experiment on a dynamic nonlinear system and the gas furnace data is given to verify the effectiveness of the proposed method.