基于多维区间T-S 模糊模型的多维运动模式预测

与传统的在欧氏空间建立系统动力学模型不同, 基于运动模式的建模是在模式运动空间中刻画系统的动力学特性. 首先, 提出一种基于粒子群优化和四叉树空间划分的多维模式运动空间的构建方法, 为了定量描述多维模式的运动, 采用多维区间数度量多维模式类别变量; 然后, 定义一种多维区间T-S 模糊模型, 并以此构建多维运动模式的预测模型; 最后, 以烧结生产过程的实际数据为例, 验证了所提出预测模型的有效性.

Multi-dimensional moving pattern prediction based on multi-dimensional interval T-S fuzzy model

In contrast to constructing the dynamical model in the traditional Euclidean space, the modeling based on moving pattern characterizes the dynamics of systems in pattern moving space. Firstly, a method of constructing multi-dimensional pattern moving space based on particle swarm optimization and quadtree partition space is proposed. For quantitatively characterizing the moving of multi-dimensional pattern, the interval vector is used to measure the multi-dimensional pattern class variable. Then, a multi-dimensional interval T-S fuzzy model is defined and used to construct the prediction model of the multi-dimensional moving pattern. Finally, the effectiveness of the proposed model is verified by the simulation based on the real sintering production process data.