CAJ | 학술논문

为了更有效地对水环境质量进行综合评价，论文提出了一种改进的 T-S（Takagi-Sugeno）模糊神经网络水质评价模型，该模型首先通过减法聚类确定模糊 C 均值聚类(FCM)的初始聚类中心和聚类数目，改善传统 FCM 算法对聚类中心初值选取的随机性及样本的敏感性，降低陷入局部最优解的可能性。将减法聚类改进的FCM算法应用到T-S模糊神经网络的特征提取中，对T-S模糊神经网络模型进行结构辨识，提高评价模型的准确性和收敛速度。通过与传统的T-S模糊神经网络比较，水质评价结果准确率更高。
위료경유효지대수배경질량진행종합평개，논문제출료일충개진적 T-S（Takagi-Sugeno）모호신경망락수질평개모형，해모형수선통과감법취류학정모호 C 균치취류(FCM)적초시취류중심화취류수목，개선전통 FCM 산법대취류중심초치선취적수궤성급양본적민감성，강저함입국부최우해적가능성。장감법취류개진적FCM산법응용도T-S모호신경망락적특정제취중，대T-S모호신경망락모형진행결구변식，제고평개모형적준학성화수렴속도。통과여전통적T-S모호신경망락비교，수질평개결과준학솔경고。
In order to evaluate the water quality more effectively, an improved T-S fuzzy neural network model was presented in this paper. The model determined the initial cluster centers and the number of clusters of FCM by subtractive clustering algorithm, and then it could reduce the randomness and sensitivity of the FCM clustering center initial selected value, and decreased the possibility of the local minimum. The improved FCM was used in the feature extraction and structure identification of the T-S fuzzy neural network to improve the accuracy and convergence rate. Compared with the traditional T-S fuzzy neural network, the water quality evaluation result worked out by the improved model was more accurate.