A modified interval type-2 Takagi-Sugeno fuzzy neural network and its convergence analysis
A modified interval type-2 Takagi-Sugeno fuzzy neural network and its convergence analysis
复制标题
修正区间2型Takagi-Sugeno模糊神经网络及其收敛性分析
DOI：
10.1016/j.patcog.2022.108861
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发表时间：
2022
影响因子：
8
通讯作者：
Jian Wang
中科院分区：
文献类型：
--
作者：
Tao Gao;Xiao Bai;Chen Wang;Liang Zhang;Jingyi Zheng;Jian Wang
本文提出了一种软最小值算法，用于计算二型模糊模型的射击强度值，使模糊模型具有求解高维问题的能力。此外，借用共轭梯度法来训练所设计的区间二型Takagi-Sugeno模糊模型。与现有的基于梯度的学习策略相比，该方案可以有效地提高模糊模型的性能。最后，对改进的区间型2型Takagi-Sugeno模糊神经网络（MIT 2 TSFNN）进行了详细的收敛性分析，证明了误差函数的梯度随着迭代次数的增加趋于零（弱收敛），模型参数（权值）序列收敛于一个不动点（强收敛）。为了验证所提出的MIT 2 TSFNN及其理论结果的有效性，六个回归和六个分类问题的仿真结果。
In this paper, to compute the firing strength values of type-2 fuzzy models, a soft version of minimum is presented, which endows the fuzzy model with the ability to solve large dimensional problems. In addition, a conjugate gradient method is borrowed to train the designed interval type-2 Takagi-Sugeno fuzzy model. Compared with the existing gradient-based learning strategy, this scheme can efficiently enhance the fuzzy model performance. Last but not least, convergence analysis for this modified interval type-2 Takagi-Sugeno fuzzy neural network (MIT2TSFNN) is conducted in detail, which proves that the gradient of the error function tends to zero with the iteration increasing (weak convergence) and the sequence of model parameters (weights) convergences to a fixed point (strong convergence). To validate the effectiveness of the proposed MIT2TSFNN and its theoretical results, simulation results of six regression and six classification problems are presented.