王静

基本情况

王静，女，境外留学博士，博士后，副教授，硕士生导师。主要从事人工智能、智能计算、机器学习、神经网络、大数据、深度学习、智能应用等领域的研究工作。博士期间获澳门科学发展基金（FDCT）之全额奖学金，并曾前往国内外多所高校进行学术交流与深度合作。现为期刊《IEEE/CAA Journal of Automatica Sinica》、《IEEE Transactions on Emerging Topics in Computational Intelligence》、《IEEE Transactions on Neural Networks and Learning Systems》、《IEEE Transactions on Industrial Electronics》和《IEEE Transactions on Fuzzy Systems》等国际期刊审稿人。

近年来先后主持和参加了多项相关科研项目的研究：国家973 项目“信息物理融合系统(Cyber-Physical System, CPS)” ，澳门科技发展基金的项目“智能控制和遥控的现代汽车系统”、“人机交互机器人系统”、“仿人机器人研究与开发”、“无线网络支持的室内全球定位系统”，广东省教育厅省级重点平台和重大科研项目“全连接模糊神经网络关键技术的研究”，广东省教育厅重点领域专项项目“面向大规模多智能体深度融合模型的研究”与“面向跨模态三维视觉融合的特征嵌入及匹配模型研究的研究”。研究成果发表 SCI期刊《IEEE Trans. on Fuzzy Systems》、《IEEE Trans on Neural Networks and Learning Systems》和《Journal of Intelligent Manufacturing》SCI和EI等期刊30余篇，并申请发明专利6项。

指导员工参与中国研究生数学建模竞赛获二等奖，指导的研究生获2022年度硕士学位毕业论文优秀奖。

教育背景

博士	于2014年毕业于澳门大学,软件工程专业
硕士	于2007年毕业于广东工业大学,计算机应用专业

主要成果

[1] Jing Wang, Shubin Lyu, C.L. Philip Chen, Huimin Zhao, et al. SPRBF-ABLS: a novel attention-based broad learning systems with sparse polynomial-based radial basis function neural networks. Journal of Intelligent Manufacturing, Jun. 2022, vol. 2022, pp. 1-16. https://doi.org/10.1007/s10845-021-01897-7 （SCI 一区）

[2] Junwei Duan; Yang Liu; Huanhua Wu; Jing Wang* (通讯作者). Broad Learning for Early Diagnosis of Alzheimer’s Disease Using FDG-PET of the Brain, Frontiers in Neuroscience, Mar, 2023. (SCI 二区Top）

[3] Lin Zheng Chun, Li Dian, , Jiang Yun Zhi, Jing Wang*（通讯作者). YOLOv3: Face Detection in Complex Environments. International Journal of Computational Intelligence Systems, 13(1), Aug. 2020, vol. 13, no.1, pp. 1153-1160. DOI: https://doi.org/10.2991/ijcis.d.200805.002.(SCI 三区）

[4] Jing Wang, Shubin Lyu, Junwei Duan, Zhengchun Lin. Sparse Enhancement Fuzzy Broad Learning System Based on Multiple Clustering Methods. Journal of Physics: Conference Series, 2022, vol. 2203, no. 1 pp. 012068.（EI）

[5] Jing Wang, Chi-Hsu Wang, and C. L. Philip. Chen. The Bounded Capacity of Fuzzy Neural Networks (FNNs) via a New Fully Connected Neural Fuzzy Inference System (F-CONFIS) with Its Applications, IEEE Trans. on Fuzzy Systems, 2014，vol. 22, no. 6, pp. 1373-1386. （SCI一区）

[6] C. L. Philip Chen（陈俊龙导师）, Jing Wang, Chi-Hsu Wang, and Long Chen. A New Learning Algorithm for a Fully Connected Fuzzy Inference System (F-CONFIS), IEEE Trans on Neural Networks and Learning Systems, vol. 25, no. 10, pp. 1741-1757, Oct. 2014.（SCI一区）

[7] Zhengchun Lin; Siyuan Li; Yunzhi Jiang; Jing Wang. Feedback Multi-scale Residual Dense Network for image super-resolution, Signal Processing： Image Communication, 2022, vol. 107, no. 116760. (SCI二区)

[8] Zhengchun Lin, Qingxing Luo, Yunzhi Jiang, Jing Wang, et al. Image defogging based on multi-input and multi-scale UNet, Signal, Image and Video Processing, Aug. 2022, pp 1-9. (SCI四区)

[9] Guangheng Wu, Junwei Duan, Jing Wang*（通讯作者）, Lu Wang, Cheng Dong and Chang wei Lv, " BroadSurv: A Novel Broad Learning System-based Approach for Survival Analysis," Proceedings of 2021 International Conference on Information, Cybernetics, and Computational Social Systems,Beijing, China, Oct, 2021.（EI）

[10] Jing Wang, C. L. Philip Chen, Zhenyuan Ma and Zhenghong Xiao *. Fuzzy Neural Networks (FNNs) Training Algorithm With Dropout via Its Equivalent Fully Connected Fuzzy Inference Systems (F-CONFIS), IEEE 2018 International Conference on Security, Pattern Analysis, and Cybernetics, 2018, pp. 99-104.（EI）

[11] Jing Wang, Yuan-Yan. Tang, L. Chen, C. L. Philip Chen and Chao-Tian Chen. A new fast-F-CONFIS training of fully-connected neuro-fuzzy inference system, 2015 IEEE International Conference on Informative and Cybernetics for Computational Social Systems, 2015, pp. 99-104.（EI）

[12] Jing Wang, Chao-Tian Chen, C. L. Philip Chen and Yong-Yuan. Yu. Mixed Radix Systems of Fully Connected Neuro-Fuzzy Inference Systems with Special Properties, 2015 IEEE International Conference on Informative and Cybernetics for Computational Social Systems, 2015, pp. 105-109.（EI）

[13] Jing Wang, C. L. Philip Chen, and Chi-Hsu Wang. On the Conjugate Gradients (CG) Training Algorithm of Fuzzy Neural Networks (FNNs) via Its Equivalent Fully Connected Neural Networks (FFNNs), 2012 IEEE International Conference on Systems, Man, and Cybernetics, 2012, pp. 2446-2451. (优秀论文奖) （EI）

[14] Jing Wang, C. L. Philip Chen, and Chi-Hsu Wang. Finding the Near Optimal Learning Rates of Fuzzy Neural Networks (FNNs) via Its Equivalent Fully Connected Neural Networks (FFNNs), IEEE International Conference of System Science and Engineering, 2012, pp. 137-142. （EI）

[15] Jing Wang, Chi-Hsu Wang and C. L. Philip Chen. Finding the Capacity of Fuzzy Neural Networks (FNNs) via Its Equivalent Fully Connected Neural Networks (FFNNs), 2011 IEEE International Conference on Fuzzy Systems, 2011, pp. 2193-2198. （EI）

[16] Jing Wang, Chi-Hsu Wang, and C. L. Philip Chen, On the BP Training Algorithm of Fuzzy Neural Networks (FNNs) via Its Equivalent Fully Connected Neural Networks (FFNNs), 2011 IEEE International Conference on Systems, Man, and Cybernetics, 2011, pp. 1376-1381. （EI）

【联系方式】

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