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Construction and Optimization of Fuzzy Relation Matrices model based-on Semi-tensor Product

机译:基于半张量积的模糊关系矩阵模型的构建与优化

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On base of semi-tensor product (STP) of matrices, this paper proposes a new and more general framework to construct a matrix-based fuzzy relation structure model for multi-input multi-output (MIMO) fuzzy control systems. The measured sampling data of inputs and outputs are assumed to be obtained from experiments. Instead of building the fuzzy logical rule sets of a fuzzy dynamical controller, the whole designing process is realized via matrix expression and algebraic algorithms. After the input and output data pairs which satisfy a fuzzy dynamic process are described by vectors, the fuzzy relation matrixes of all input and output variables for each data pair are calculated respectively so its fuzzy relation matrix can be got. Final fuzzy relation matrix of all measured data can be achieved by fuzzy disjunction operation and then optimized by the fuzzy α -interception matrix. The new technique gives one general design method to obtain fuzzy relation matrix expression of multiple variables fuzzy control systems. It is particularly suitable to design non-decomposable multi-output fuzzy controllers, which is not solvable directly by the traditional decomposed control design methods.
机译:基于矩阵的半张量积(STP),本文提出了一个新的,更通用的框架,用于为多输入多输出(MIMO)模糊控制系统构建基于矩阵的模糊关系结构模型。假定输入和输出的测量采样数据是从实验中获得的。无需建立模糊动态控制器的模糊逻辑规则集,而是通过矩阵表达式和代数算法来实现整个设计过程。通过向量描述满足模糊动态过程的输入和输出数据对后,分别计算每个数据对的所有输入和输出变量的模糊关系矩阵,从而得到其模糊关系矩阵。所有测量数据的最终模糊关系矩阵可以通过模糊析取运算获得,然后通过模糊α拦截矩阵进行优化。该新技术为获得多变量模糊控制系统的模糊关系矩阵表达式提供了一种通用的设计方法。特别适合设计不可分解的多输出模糊控制器,这种控制器不能通过传统的分解控制设计方法直接解决。

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