Reformative Noise-Immune Neural Network for Equality-Constrained Optimization Applied to Image Target Detection
文献类型：期刊论文
| 作者 | Ying Liufu1,3; Jin, Long2,3; Xu, Jinqiang4; Xiao, Xiuchun4; Fu, Dongyang4 |
| 刊名 | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 10期号:2页码:973-984 |
| ISSN号 | 2168-6750 |
| 关键词 | Optimization Computational modeling Mathematical model Numerical models Convergence Neural networks Robustness Reformative noise-immune neural network (RNINN) equality-constrained optimization noise-resistance robustness |
| DOI | 10.1109/TETC.2021.3057395 |
| 通讯作者 | Jin, Long(longjin@ieee.org) |
| 英文摘要 | Equality-constrained optimization problem captures increasing attention in the fields of computer science, control engineering, and applied mathematics. Almost all of the relevant issues suffer from kinds of intense or weak noises during the solving process, so that how to realize the noise deduction even noise elimination has increasingly become a sticky and significant problem. A lot of corresponding solving models are established for the equality-constrained optimization problem. However, the majority of them can find the optimal solution to a certain extent in the absence of noise disturbance, but few can behave a brilliant noise-resistance proficiency. On account of this discovery, a reformative noise-immune neural network (RNINN) model is constructed. In addition, the conventional gradient-based recursive neural network model and the zeroing recursive neural network model are presented to compare with the proposed RNINN model on convergence properties and noise-resistance capabilities. Lastly, the relative numerical experiment simulation and image target detection application are implemented to further elaborate on the robustness and efficiency of the RNINN model. |
| 资助项目 | National Key Research and Development Program of China[2017YFE0118900] ; research project of Huawei Mindspore Academic Award Fund of Chinese Association of Artificial Intelligence[CAAIXSJLJJ-2020-009A] ; Team Project of Natural Science Foundation of Qinghai Province, China[2020-ZJ-903] ; Key Laboratory of IoT of Qinghai[2020-ZJ-Y16] ; Natural Science Foundation of Gansu Province, China[20JR10RA639] ; Natural Science Foundation of Chongqing (China)[cstc2020jcyj-zdxmX0028] ; Research and Development Foundation of Nanchong (China)[20YFZJ0018] ; CAS Light of West China Program ; Chongqing Key Laboratory of Mobile Communications Technology[cqupt-mct-202004] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000808083300036 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/16317]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Jin, Long |
| 作者单位 | 1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China 2.Lanzhou Univ, Dept Comp Sci, Lanzhou 730000, Gansu, Peoples R China 3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Beijing 100049, Peoples R China 4.Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ying Liufu,Jin, Long,Xu, Jinqiang,et al. Reformative Noise-Immune Neural Network for Equality-Constrained Optimization Applied to Image Target Detection[J]. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING,2022,10(2):973-984. |
| APA | Ying Liufu,Jin, Long,Xu, Jinqiang,Xiao, Xiuchun,&Fu, Dongyang.(2022).Reformative Noise-Immune Neural Network for Equality-Constrained Optimization Applied to Image Target Detection.IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING,10(2),973-984. |
| MLA | Ying Liufu,et al."Reformative Noise-Immune Neural Network for Equality-Constrained Optimization Applied to Image Target Detection".IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 10.2(2022):973-984. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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