刘源泉，男，硕士研究生，毕业于大连工业大学，研究方向为机器学习，算法优化，模糊数据分析。研究领域为分数阶导数，梯度下降，区间模糊数据处理。具备扎实的编程技能，熟练掌握Java、Matlab，具有机器学习、深度学习及算法优化的应用经验，发表多篇学术论文：

[1] Yuanquan Liu et al. An interval neural network-based Caputo fractional-order extreme learning machine applied to classification. Applied Soft Computing 167 (2024): 112310. （第一作者，SCI一区，已发表） 

[2] Yan Liu, Yuanquan Liu et al. A Novel Neuro-fuzzy Learning Algorithm for First-Order Takagi– Sugeno Fuzzy Model: Caputo Fractional-Order Gradient Descent Method. International Journal of Fuzzy Systems 26.8 (2024): 2616-2631. （第二作者， SCI 三区，已发表）

[3] Qiang Shao, Yuanquan Liu et al. A smoothing interval neural networks-based Caputo fractionalorder gradient learning algorithm. International Journal of Machine Learning and Cybernetics (2024): 1-17.（第二作者，SCI 三区，已发表） 

[4] Yan Liu, Rui Wang, Yuanquan Liu et al. A novel learning approach to remove oscillations in First‐Order Takagi–Sugeno fuzzy System: gradient Descent‐Based Neuro‐Fuzzy algorithm using smoothing group lasso regularization. Advanced Theory and Simulations 7.2 (2024): 2300545. （第三作者，SCI 四区，已发表） 

[5] Jian Li, Qiang Shao, Yuanquan Liu et al. A Novel Smoothing Framework for Interval-zeroorder Takagi-Sugeno Fuzzy Neural Networks: Combining Interval Analysis with Zero-order TS Systems. 2024 International Conference on New Trends in Computational Intelligence (NTCI). IEEE, 2024. （第三作者，EI国际会议，已发表） 

Rui Wang, Yan Liu, Yuanquan Liu et al. A modified learning method for first-order takagisugeno fuzzy system: Gradient-based neuro-fuzzy algorithm employing smoothing group lasso regularization. 2023 International Conference on New Trends in Computational Intelligence (NTCI). Vol. 1. IEEE, 2023. （第三作者，EI国际会议，已发表）