[1] 黄一鸣, 雷航, 李晓瑜. 量子机器学习算法综述[J]. 计算机学报, 2018, 41(1):145-163. Huang Y M, Lei H, Li X Y. Review of quantum machine learning algorithms[J]. Chinese Journal of Computers, 2018, 41(1):145-163. (in Chinese)
[2] 汤深伟, 贾瑞玉. 基于改进粒子群算法的k均值聚类算法[J]. 计算机工程与应用, 2019, 55(18):140-145. Tang S W, Jia R Y. K-means clustering algorithm based on improved particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2019, 55(18):140-145. (in Chinese)
[3] 刘洋, 王慧琴, 张小红. 结合蚁群算法的改进粗糙K均值聚类算法[J]. 数据采集与处理, 2019, 34(2):341-348. Liu Yang, Wang H Q, Zhang X H. Improved rough K-means clustering algorithm combined with ant colony algorithm[J]. Journal of Data Acquisition & Processing, 2019, 34(2):341-348. (in Chinese)
[4] Bezdek J C. Pattern recognition with fuzzy objective function algorithms[J]. New York:Plenum Press, 1981.
[5] Jiang Y, Chung F L, Wang S. Enhanced fuzzy partitions vs data randomness in FCM[J]. Journal of Intelligent and Fuzzy Systems, 2014, 27(4):1639-1648.
[6] 高云龙, 杨程宇, 王志豪, 等. 簇间可分的鲁棒模糊C均值聚类算法[J]. 电子与信息学报, 2019, 5(1):1114-1121. Gao Y L, Yang C Y, Wang Z H, et al. Robust fuzzy C-means clustering algorithm for separable clusters[J]. Journal of Electronics and Information, 2019, 5(1):1114-1121. (in Chinese)
[7] Karayiannis N B, Mi G W. Growing radial basis neural networks:Merging supervised and unsupervised learning with network growth techniques[J]. IEEE Transactions on Neural Networks, 1997, 8(6):1492-1506.
[8] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6):1229-1251. Zhou F Y, Jin L P, Dong J. A review of convolution neural networks[J]. Chinese Journal of Computers, 2017, 40(6):1229-1251. (in Chinese)
[9] 安亚利, 周水生, 陈丽, 等. 鲁棒支持向量机及其稀疏算法[J]. 西安电子科技大学学报, 2019, 46(1):64-72. An Y L, Zhou S S, Chen L, et al. Robust support vector machine and its sparse algorithm[J]. Journal of Xi'an University of Electronic Science and Technology, 2019, 46(1):64-72. (in Chinese)
[10] Keerthi S S, Shevade S K, Bhattacharyya C, et al. Improvements to Platt's SMO algorithm for SVM classifier design[J]. Neural Computation, 2001, 13(3):637.
[11] 谢文博, 李鹤, 张孟禹, 等. 前件变量未知的T-S模糊系统输出反馈控制[J]. 控制与决策, 2019, 34(9):1876-1884. Xie W B, Li H, Zhang M Y, et al. Output feedback control of Tmurs fuzzy system with unknown antecedent variables[J]. Control and Decision, 2019,34(9):1876-1884. (in Chinese)
[12] 顾晓清, 王士同, 倪彤光, 等. 具有强解释性的贝叶斯MA型模糊系统[J]. 控制与决策, 2018, 33(1):16-26. Gu X Q, Wang S T, Ni T G, et al. Bayesian MA fuzzy system with strong interpretation[J]. Control and Decision, 2018, 33(1):16-26. (in Chinese)
[13] 顾晓清, 王士同. 知识嵌入的贝叶斯MA型模糊系统[J]. 计算机研究与发展, 2017, 54(5):998-1011. Gu X Q, Wang S T. Knowledge embedded Bayesian MA fuzzy system[J]. Computer Research and Development, 2017, 54(5):998-1011. (in Chinese)
[14] Deng Z H, Jiang Y Z, Choi K S, et al. Knowledge-leverage-based TSK fuzzy system modeling[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(8):1200-1212.
[15] 杭文龙, 梁爽, 刘解放, 等. 贝叶斯L2型TSK模糊系统[J]. 控制与决策, 2017, 32(10):1871-1878. Hang W L, Liang S, Liu J F, et al. Bayesian L 2 TSK fuzzy system[J]. Control and Decision, 2017, 32(10):1871-1878. (in Chinese)
[16] Jiang Y, Deng Z, Chung F L, et al. Multi-task TSK fuzzy system modeling using inter-task correlation information[J]. Information Sciences, 2015, 298:512-533.
[17] Jiang Y, Chung F L, Ishibuchi H. Multitask TSK fuzzy system modeling by mining intertask common hidden structure[J]. IEEE Transactions on Cybernetics, 2015, 45(3):548-561.
[18] Kinoshita S, Udaka S, Shimamoto M. Studies on amino acid fermentation, Part I. production of L-glutamic acid by various microorganisms[J]. Journal of General and Applied Microbiology, 1957, 3:193-205.
[19] Caruana R. Multitask learning[J]. Machine Learning, 1997, 28(1):41-75.
[20] 史荧中, 邓赵红, 钱鹏江, 等. 基于共享矢量链的多任务概念漂移分类方法[J]. 控制与决策, 2018, 33(7):1215-1222. Shi Y Z, Deng Z H, Qian P J, et al. Classification method of multitask concept drift based on shared vector chain[J]. Control and Decision, 2018, 33(7):1215-1222. (in Chinese)
[21] Ji Y, Sun S. Multitask multiclass support vector machines:model and experiments[J]. Pattern Recognition, 2013, 46(3):914-924.
[22] Gu Q, Zhou J. Learning the shared subspace for multi-task clustering and transductive transfer classification[C]//Proceedings of the International Conference Data Mining (ICDM). Miami, USA, 2009:59-168.
[23] 张晓彤, 张宪超, 刘晗. 基于特征和实例迁移的加权多任务聚类[J]. 计算机学报, 2019, 42(12):2614-2630. Zhang X T, Zhang X C, Liu H. Weighted multitask clustering based on feature and instance migration[J]. Chinese Journal of Computers, 2019, 42(12):2614-2630. (in Chinese)
[24] Zhang Z, Zhou J. Multi-task clustering via domain adaptation[J]. Pattern Recognition, 2012, 45(1):465-473.
[25] Fang J, Ji S, Xue Y, et al. Multitask classification by learning the task relevance[J]. IEEE Signal Processing Letters, 2008, 15:593-596.
[26] 张苗辉, 张博, 高诚诚. 一种多任务的卷积神经网络目标分类算法[J]. 激光与光电子学进展, 2019, 23:222-229. Zhang M H, Zhang B, Gao C C. A multitasking convolution neural network target classification algorithm[J]. Advances in Laser and Optoelectronics, 2019, 23:222-229. (in Chinese)
[27] Wang H. A new sparse multi-task regression and feature selection method to identify brain imaging predictors for memory performance[C]//Proceedings of the IEEE International Conference on Computer Vision, 2011:557-572.
[28] Solnon M, Arlot S, Bach F. Multi-task regression using minimal penalties[J]. Journal of Machine Learning Research, 2012, 13:2773-2812.
[29] Zhou J, Yuan L, Liu J, et al. A multi-task learning formulation for predicting disease progression[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 2011:21-24.
[30] 任建华, 赵凯龙, 刘欣宜. 模糊控制理论在风电机组变桨控制系统中的应用[J]. 现代电子技术, 2019, 42(23):130-134. Ren J H, Zhao K L, Liu X Y. Application of fuzzy control theory in variable pitch control system of wind turbine[J]. Modern Electronic Technology, 2019, 42(23):130-134. (in Chinese)
[31] Chung F L, Deng Z H, Wang S T. An adaptive fuzzy-inference-rule-based flexible model for automatic elastic image registration[J]. IEEE Transactions on Fuzzy Systems, 2009, 17(5):995-1010.
[32] 张代远, 吕鹏. 基于零代价函数神经网络的神经-模糊系统与应用[J]. 计算机应用, 2006, 26(A2):208-210. Zhang D Y, Lü P. Neural-fuzzy system based on zero-cost function neural network and its application[J]. Computer applications, 2006, 26(A2):208-210. (in Chinese)
[33] Wang N, Meng J E. Direct adaptive fuzzy tracking control of marine vehicles with fully unknown parametric dynamics and uncertainties[J]. IEEE Transactions on Control Systems Technology, 2016, 24(5):1845-1852.
[34] 王华强, 张博扬. 基于AFSA-BP算法的模糊PID控制器在DMF回收系统中的应用[J]. 测控技术, 2019, 38(10):80-84. Wang H Q, Zhang B Y. Application of fuzzy PID controller based on AFSA-BP algorithm in DMF recovery system[J]. Measurement and Control Technology, 2019, 38(10):80-84. (in Chinese)
[35] Deng Z H, Choi K S, Chung F L, et al. Scalable TSK fuzzy modeling for very large datasets using minimal-enclosing-ball approximation[J]. IEEE Transactions on Fuzzy Systems, 2011, 19(2):210-226.
[36] Juang C F, Chiu S H, Shiu S J. Fuzzy system learned through fuzzy clustering and support vector machine for human skin color segmentation[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans, 2007, 37(6):1077-1087.
[37] Juang C F, Hsieh C D. TS-fuzzy system-based support vector regression[J]. Fuzzy Sets and Systems, 2009, 160(17):2486-2504.
[38] Krysmann M. Takagi-Sugeno-Kanga fuzzy fusion in dynamic multi-classifier system[C]//Proceedings of the 2nd World Congress on Electrical Engineering and Computer Systems and Science (EECSS'16), 2016:108.1-108.7.
[39] Ayres A, Zuben V. Multitask learning applied to evolving fuzzy-rule-based predictors[J]. Evolving Systems, 2019. doi:10.1007/s12530-019-09300-w.
[40] Wang J, Lin D, Deng Z, et al. Multitask TSK fuzzy system modeling by jointly reducing rules and consequent parameters[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2019. doi:10.1109/TSMC.2019.2930616.
[41] Chiu S L. Fuzzy model identification based on cluster estimation[J]. Journal of Intelligent and Fuzzy Systems, 1994, 2(3):267-278.
[42] Leski J M. Fuzzy c-means clustering and its application to a fuzzy rule-based classifier:toward good generalization and good interpretability[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(4):802-812.
[43] Ito K, Nakano R. Optimizing support vector regression hyperparameters based on crossvalidation[C]//Proceedings of the International Joint Conference on Neural Networks, 2003:2077-2082.
[44] Fan R E, Chen P H, Lin C J. Working set selection using second order information for training support vector machines[J]. Journal of Machine Learning Research, 2005, 6:1889-1918.
[45] Kim D, Sra S, Dhillon I S. A scalable trust-region algorithm with application to mixednorm regression[C]//Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010:21-24.
