一种基于量子遗传算法的扩展T-S 模型辨识

在传统T-S模型的基础上，提出一种扩展T-S模型。该模型由一组模糊规则组成，由规则前件实现输入空间的划分，将成员函数及其函数变换引入规则后件以实现对输入子空间的非线性映射。对于该模型的建立，使用改进量子遗传算法优化规则前件，递推最小二乘法确定规则后件参数。通过对两个典型非线性系统辨识，仿真结果表明了该模型可以显著提高辨识精度，且具有很好的泛化性能。

An expanded T-S model identification based on quantum genetic algorithm

An expanded T-S model is proposed based on the conventional T-S model. This model is comprised of a set
of fuzzy rules. According to the premise part of the rules, the input space can be partitioned, and the membership values
and their transformations are introduced in the consequent part of the rules to express the nonlinear mapping relation in the
input subspace. To construct the model, the improved quantum genetic algorithm is used to optimize the premise part of the
rules, and the recursive least squares method is used to determine the parameters in the consequent part of the rules. Through
the identification of two nonlinear systems, simulation results show that the proposed model can improve the approximation
accuracy and have excellent generalization ability.