To realize the adaptive identification of T–S fuzzy model structure, we propose a variable structure T–S fuzzy model algorithm. Compare to traditional multi-input single-output in the T–S fuzzy model, we extend single-output fuzzy rules to multi-dimensional output fuzzy rules, which has the advantage that all multi-dimensional outputs share the same premise parameter; Then the joint block structure sparse ridge regression model is used to realize the identification of the consequent parameter, which provides a regression model. In this model, some regression coefficient blocks with small contribution will be reduced to zero accurately, while maintaining high prediction accuracy. Otherwise, the Fuzzy Expectation Maximization (FEM) is proposed to coarse fine the premise parameter. Finally, the variable structure T–S fuzzy model is applied to the maneuvering target tracking without filter. The simulation results show that the proposed algorithm is more accurate and stable than the Interacting Multiple Model (IMM), Interacting Multiple Model Unscented Kalman Filtering (IMMUKF), Interacting Multiple Model Rao-Blackwellized Particle Filtering (IMMRBPF) and T–S Fuzzy semantic Model (TS-FM) algorithms in dealing with uncertain problems in nonlinear maneuvering target tracking systems.

为了实现T-S模糊模型结构的自适应识别，我们提出了一种变结构T-S模糊模型算法。与传统的T-S模糊模型中的多输入单输出相比，我们将单输出模糊规则扩展为多维输出模糊规则，具有所有多维输出共享相同前提参数的优点；然后利用联合块结构稀疏岭回归模型实现对后件参数的识别，提供了回归模型。在该模型中，一些贡献较小的回归系数块将准确地归零，同时保持较高的预测精度。否则，提出了模糊期望最大化（FEM）来粗化前提参数。最后，将变结构T-S模糊模型应用于无滤波机动目标跟踪。仿真结果表明，所提出的算法比交互多模型（IMM）、交互多模型无迹卡尔曼滤波（IMMUKF）、交互多模型Rao-Blackwellized粒子滤波（IMMRBPF）和T-S模糊语义模型更加准确和稳定。 （TS-FM）算法处理非线性机动目标跟踪系统中的不确定问题。