应用科学学报 ›› 2020, Vol. 38 ›› Issue (5): 742-760.doi: 10.3969/j.issn.0255-8297.2020.05.007
• 智能计算新技术 • 上一篇
蒋亦樟1, 华蕾1, 张群1,2, 钱鹏江1, 夏开建1,3,4
收稿日期:
2020-03-31
发布日期:
2020-10-14
通信作者:
夏开建,博士,高工,研究方向为计算智能方法.E-mail:xiakaijian@163.com
E-mail:xiakaijian@163.com
基金资助:
JIANG Yizhang1, HUA Lei1, ZHANG Qun1,2, QIAN Pengjiang1, XIA Kaijian1,3,4
Received:
2020-03-31
Published:
2020-10-14
摘要: 该文提出了一种多任务Takagi-Sugeno-Kang(TSK)模糊系统建模方法.首先给出了一种新的多任务模糊c均值聚类算法,能够有效提取所有任务之间的公共信息和每个任务的私有信息,进而利用所得的聚类中心构建多任务TSK模糊系统的前件参数.其次设计了一种具备多任务协同学习机制的后件参数优化方法,可以优化多任务TSK模糊系统的后件参数.最后基于优化的前后件参数,构建出具体多任务模糊聚类方法驱动的多任务TSK模糊系统模型(multi-task fuzzy c-means based multi-task TSK fuzzy system,MTFCM-MT-TSK-FS)以用于实际应用.分别在合成和真实数据集上进行实验,结果验证了该模型的有效性.
中图分类号:
蒋亦樟, 华蕾, 张群, 钱鹏江, 夏开建. 多任务模糊聚类驱动的多任务TSK模糊系统模型[J]. 应用科学学报, 2020, 38(5): 742-760.
JIANG Yizhang, HUA Lei, ZHANG Qun, QIAN Pengjiang, XIA Kaijian. Multi-task Fuzzy Clustering Based Multi-task TSK Fuzzy System[J]. Journal of Applied Sciences, 2020, 38(5): 742-760.
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