Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926769
Saisai Yu, Jianlong Qiu, Xin Bao, Ming Guo, Xiangyong Chen, Jianqiang Sun
In the traditional movie recommendation, because the features of users and movies are not considered, only the users' ratings of movies are considered, so there is a problem that the recommendation is not accurate enough. In response to this problem, this paper proposes a movie rating prediction recommendation algorithm based on XGBoost-DNN. First, XG-Boost is used to screen user features and movie features, and the features that have a great impact on movie rating prediction are screened out, and then the screened features are used as the input of DNN, the user network, and the movie network is trained to obtain the user feature vector and movie feature vector respectively, and then the user's predicted rating of the movie is obtained through the neural network, and finally compared with LightGBM, SVR, KNN, and RandomForest, this paper proposed XGBoost-DNN model reduces the MSE indicator by 0.223, 0.75, 0.451, and 0.306 respectively, which effectively improves the accuracy of rating prediction, and thus improves the accuracy of movie recommendation.
{"title":"Movie Rating Prediction Recommendation Algorithm based on XGBoost-DNN","authors":"Saisai Yu, Jianlong Qiu, Xin Bao, Ming Guo, Xiangyong Chen, Jianqiang Sun","doi":"10.1109/ICIST55546.2022.9926769","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926769","url":null,"abstract":"In the traditional movie recommendation, because the features of users and movies are not considered, only the users' ratings of movies are considered, so there is a problem that the recommendation is not accurate enough. In response to this problem, this paper proposes a movie rating prediction recommendation algorithm based on XGBoost-DNN. First, XG-Boost is used to screen user features and movie features, and the features that have a great impact on movie rating prediction are screened out, and then the screened features are used as the input of DNN, the user network, and the movie network is trained to obtain the user feature vector and movie feature vector respectively, and then the user's predicted rating of the movie is obtained through the neural network, and finally compared with LightGBM, SVR, KNN, and RandomForest, this paper proposed XGBoost-DNN model reduces the MSE indicator by 0.223, 0.75, 0.451, and 0.306 respectively, which effectively improves the accuracy of rating prediction, and thus improves the accuracy of movie recommendation.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116176811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926951
Zhaoyang Feng, Xing Wang, Deqian Fu
Event extraction aims to extract structured triggers and arguments from unstructured text. However, the accuracy of named entity recognition will directly affect the performance of event argument role recognition, which results in error propagation. Meanwhile, treating the event extraction task as a classification task ignores semantic information in sentences. In the paper, we propose a dual machine reading comprehension model (Dual-MRC) for event extraction, which converts the classification task into a span extraction task. The model consists of the part of speech of the candidate argument on the left and the imperative sentence on the right to form a question template, dramatically improving the ability of event extraction. Dual-MRC achieves an F1 value of 74.6% in the event trigger extraction and classification task.Our model performs excellently in the case of data-low scenarios, demonstrating the advantages of machine reading comprehension. Experimental results show that our method is effective on the ACE 2005 dataset, especially for multi-word trigger extraction. In addition, we publish a Chinese mine accident annotation dataset. To the best of our knowledge, this is the first Chinese mine accident event dataset, and we verify the performance of the model in Chinese event extraction on this dataset.
{"title":"Dual Machine Reading Comprehension for Event Extraction","authors":"Zhaoyang Feng, Xing Wang, Deqian Fu","doi":"10.1109/ICIST55546.2022.9926951","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926951","url":null,"abstract":"Event extraction aims to extract structured triggers and arguments from unstructured text. However, the accuracy of named entity recognition will directly affect the performance of event argument role recognition, which results in error propagation. Meanwhile, treating the event extraction task as a classification task ignores semantic information in sentences. In the paper, we propose a dual machine reading comprehension model (Dual-MRC) for event extraction, which converts the classification task into a span extraction task. The model consists of the part of speech of the candidate argument on the left and the imperative sentence on the right to form a question template, dramatically improving the ability of event extraction. Dual-MRC achieves an F1 value of 74.6% in the event trigger extraction and classification task.Our model performs excellently in the case of data-low scenarios, demonstrating the advantages of machine reading comprehension. Experimental results show that our method is effective on the ACE 2005 dataset, especially for multi-word trigger extraction. In addition, we publish a Chinese mine accident annotation dataset. To the best of our knowledge, this is the first Chinese mine accident event dataset, and we verify the performance of the model in Chinese event extraction on this dataset.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124012720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926772
Shuai Zhang, Jun Xing, Xinzhe Wang, Jianchao Fan
Marine oil spill spreads rapidly and has a long-term impact. Once it occurs, it will cause severe damage to the ecological environment. Synthetic Aperture Radar (SAR) is widely used in marine oil spill monitoring due to its all-weather and all-day characteristics. However, the contrast of different SAR images is inconsistent, making it difficult for the network to learn valuable features. To address this issue, this paper proposes an improved YOLOX-S (IYOLOX-S) model for marine oil spill detection. The model enhances image contrast by a truncated linear stretch module, uses CspDarknet and PANnet to extract image features, and obtains oil spill detection results through Decoupled Head. First, a truncated linear stretching module is added, which can improve the image contrast. It also highlights the characteristics of oil spill areas to enhance the networks learning ability. Second, the proposed score loss into the global loss function enhances the learning ability of the model and improves the detection accuracy. Experiments are carried out on the collected oil spill dataset, and the test sets average precision (AP) is 90.02%. The experimental results show that the improved YOLOX-S model accurately identifies oil spill areas.
{"title":"Improved YOLOX-S Marine Oil Spill Detection Based on SAR Images","authors":"Shuai Zhang, Jun Xing, Xinzhe Wang, Jianchao Fan","doi":"10.1109/ICIST55546.2022.9926772","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926772","url":null,"abstract":"Marine oil spill spreads rapidly and has a long-term impact. Once it occurs, it will cause severe damage to the ecological environment. Synthetic Aperture Radar (SAR) is widely used in marine oil spill monitoring due to its all-weather and all-day characteristics. However, the contrast of different SAR images is inconsistent, making it difficult for the network to learn valuable features. To address this issue, this paper proposes an improved YOLOX-S (IYOLOX-S) model for marine oil spill detection. The model enhances image contrast by a truncated linear stretch module, uses CspDarknet and PANnet to extract image features, and obtains oil spill detection results through Decoupled Head. First, a truncated linear stretching module is added, which can improve the image contrast. It also highlights the characteristics of oil spill areas to enhance the networks learning ability. Second, the proposed score loss into the global loss function enhances the learning ability of the model and improves the detection accuracy. Experiments are carried out on the collected oil spill dataset, and the test sets average precision (AP) is 90.02%. The experimental results show that the improved YOLOX-S model accurately identifies oil spill areas.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116385377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926831
Zhengtai Xie, Jialiang Fan, Xiujuan Du, Long Jin
Thanks to the transformation and upgrading of traditional manufacturing, robotic manipulators have been de-veloping rapidly and attracted extensive attention. In this back-ground, this paper generalizes the control problem of redun-dant robotic systems into a quadratic programming (QP)-based control scheme. Then, discussions are carried out on such a scheme, succinctly describing and analyzing selected examples of each component with different emphases. Subsequently, a corresponding controller is presented to realize the kinematic control of redundant manipulators with abundant simulative results. Finally, the principles of robotic physical experiments are clearly explained and analyzed.
{"title":"Revisiting QP-based Control Schemes for Redundant Robotic Systems with Different Emphases","authors":"Zhengtai Xie, Jialiang Fan, Xiujuan Du, Long Jin","doi":"10.1109/ICIST55546.2022.9926831","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926831","url":null,"abstract":"Thanks to the transformation and upgrading of traditional manufacturing, robotic manipulators have been de-veloping rapidly and attracted extensive attention. In this back-ground, this paper generalizes the control problem of redun-dant robotic systems into a quadratic programming (QP)-based control scheme. Then, discussions are carried out on such a scheme, succinctly describing and analyzing selected examples of each component with different emphases. Subsequently, a corresponding controller is presented to realize the kinematic control of redundant manipulators with abundant simulative results. Finally, the principles of robotic physical experiments are clearly explained and analyzed.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122794345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, an adaptive finite-time neural network tracking control problem for uncertain non-strict feedback systems is studied. For unknown nonlinear functions, they are approximated using neural networks. Under the framework of adaptive backstepping, a finite-time tracking controller based on a non-strict feedback system is designed. Unlike existing finite-time results, the proposed method can guarantee that the output of the system tracks the reference signal in a shorter time, and further, the tracking error is guaranteed to be confined to a small origin domain, while all signals in the closed-loop system are bounded and fast practical finite-time stablility. Finally, simulation example is given to exhibit the effectiveness of the presented technique.
{"title":"Adaptive Finite-Time Neural Network Control for Non-strict Feedback Systems","authors":"Chunting Xue, Feng Zhao, Xiangyong Chen, Jianlong Qiu, Guanzheng Wang, Tong Wang","doi":"10.1109/ICIST55546.2022.9926902","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926902","url":null,"abstract":"In this paper, an adaptive finite-time neural network tracking control problem for uncertain non-strict feedback systems is studied. For unknown nonlinear functions, they are approximated using neural networks. Under the framework of adaptive backstepping, a finite-time tracking controller based on a non-strict feedback system is designed. Unlike existing finite-time results, the proposed method can guarantee that the output of the system tracks the reference signal in a shorter time, and further, the tracking error is guaranteed to be confined to a small origin domain, while all signals in the closed-loop system are bounded and fast practical finite-time stablility. Finally, simulation example is given to exhibit the effectiveness of the presented technique.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123016114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926779
Yu Wang, Zhi Qiao, Junru Yin, Mingliang Zhang
Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of multiple autonomous systems, this paper designs and organizes them into autonomous system community. Within the community, a precomputing multi-link generation mechanism is proposed based on genetic algorithm. Before the inter domain routing system is attacked, the link set can be generated as complete as possible in advance. Once an attack occurs, the community will quickly select proper precomputing links to replace the failed ones, in order to ensure the stability of inter domain communication. The effectiveness of the mechanism has been proved in function and performance through simulation experiments.
{"title":"Design and Implementation of Links Generation for Inter Domain Routing System","authors":"Yu Wang, Zhi Qiao, Junru Yin, Mingliang Zhang","doi":"10.1109/ICIST55546.2022.9926779","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926779","url":null,"abstract":"Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of multiple autonomous systems, this paper designs and organizes them into autonomous system community. Within the community, a precomputing multi-link generation mechanism is proposed based on genetic algorithm. Before the inter domain routing system is attacked, the link set can be generated as complete as possible in advance. Once an attack occurs, the community will quickly select proper precomputing links to replace the failed ones, in order to ensure the stability of inter domain communication. The effectiveness of the mechanism has been proved in function and performance through simulation experiments.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123580343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926834
Jianqiao Yu, Hui Liang, Yi Sun
X-ray Computed Tomography (CT) is widely used in clinical diagnosis. However, the requirement of numerous projections collected in a full-angular range hinders CT image-guided applications such as real-time biopsy. This paper mainly discusses the most challenging single view CT reconstruction problem to speed up the CT-guided clinical workflow. We propose a deep learning approach for single-view CT reconstruction guided by Filtered Back Projection (FBP) algorithm which makes the single view reconstruction accurate, fast and interpretable. We formulate an end-to-end framework that contains the projection generation network to predict sufficient projections from a single view, the FBP layer to obtain coarse CT volume, and the CT fine-tuning network to output the final CT volume. We carefully design our training strategy to ensure the network towards CT reconstruction. Our experiments on the public 4D CT datasets prove that our method achieves state-of-the-art performance.
x射线计算机断层扫描(CT)广泛应用于临床诊断。然而，在全角度范围内收集大量投影的要求阻碍了CT图像引导的应用，如实时活检。本文主要讨论最具挑战性的单视图CT重建问题，以加快CT引导的临床工作流程。提出了一种基于滤波后投影(filter Back Projection, FBP)算法的深度学习单视图CT重建方法，使单视图重建准确、快速、可解释。我们制定了一个端到端框架，其中包含投影生成网络(用于从单个视图预测足够的投影)、FBP层(用于获得粗CT体积)和CT微调网络(用于输出最终CT体积)。我们精心设计了训练策略，以确保网络向CT重建方向发展。我们在公共4D CT数据集上的实验证明了我们的方法达到了最先进的性能。
{"title":"Deep Learning Single View Computed Tomography Guided by FBP Algorithm","authors":"Jianqiao Yu, Hui Liang, Yi Sun","doi":"10.1109/ICIST55546.2022.9926834","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926834","url":null,"abstract":"X-ray Computed Tomography (CT) is widely used in clinical diagnosis. However, the requirement of numerous projections collected in a full-angular range hinders CT image-guided applications such as real-time biopsy. This paper mainly discusses the most challenging single view CT reconstruction problem to speed up the CT-guided clinical workflow. We propose a deep learning approach for single-view CT reconstruction guided by Filtered Back Projection (FBP) algorithm which makes the single view reconstruction accurate, fast and interpretable. We formulate an end-to-end framework that contains the projection generation network to predict sufficient projections from a single view, the FBP layer to obtain coarse CT volume, and the CT fine-tuning network to output the final CT volume. We carefully design our training strategy to ensure the network towards CT reconstruction. Our experiments on the public 4D CT datasets prove that our method achieves state-of-the-art performance.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131499747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926934
Jie Li, Wen Zhang, Pu Cheng, Yujing Wang, Xiaoyu Du
Mobile edge computing (MEC) is an emerging technology that uses wireless networks to provide resource services for resource-constrained mobile devices. To address the problems of mobile device the percentage of offloading and system utility, the maximum the percentage of offloading and a system utility model are constructed in this paper. This model redefines the portion of task offloading combined with the task offloading scenario, making the offloading task more inclined to important user tasks and improving the quality of the task offloading. At the same time, an adaptive binary whale resource allocation (ABWRA) scheme is proposed to optimize the task offloading strategy and channel allocation strategy. In the evaluation, this paper simulates the computation offloading of ABWRA and existing works in the same scenario. Simulation results show that the proposed ABWRA scheme improves the system utility by 5.4% and the unloading rate by 9.8% compared with the BWRA algorithm.
{"title":"Adaptive Binary Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing","authors":"Jie Li, Wen Zhang, Pu Cheng, Yujing Wang, Xiaoyu Du","doi":"10.1109/ICIST55546.2022.9926934","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926934","url":null,"abstract":"Mobile edge computing (MEC) is an emerging technology that uses wireless networks to provide resource services for resource-constrained mobile devices. To address the problems of mobile device the percentage of offloading and system utility, the maximum the percentage of offloading and a system utility model are constructed in this paper. This model redefines the portion of task offloading combined with the task offloading scenario, making the offloading task more inclined to important user tasks and improving the quality of the task offloading. At the same time, an adaptive binary whale resource allocation (ABWRA) scheme is proposed to optimize the task offloading strategy and channel allocation strategy. In the evaluation, this paper simulates the computation offloading of ABWRA and existing works in the same scenario. Simulation results show that the proposed ABWRA scheme improves the system utility by 5.4% and the unloading rate by 9.8% compared with the BWRA algorithm.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132492600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-14DOI: 10.1109/ICIST55546.2022.9926859
A. Abubakar, Yuming Feng, A. Ibrahim
In this article, inspired by the overwhelming suc-cesses recorded by the inertial effect on existing iterative methods, we propose an inertial projection conjugate gradient (CG) method for solving nonlinear monotone operator equations with convex constraints. As a starting point, the inertial step is added to an existing CG method called PCG with the aim of speeding up its convergence. Under appropriate assumptions, the global convergence of the method is established. Numerical results are reported and in comparison with the PCG method, the effect of the inertial term is clearly seen as the propose approach outperforms the PCG method based on all metrics considered. This is evident that the inertial term has really performed its duty as expected.
{"title":"Inertial Projection Method for Solving Monotone Operator Equations","authors":"A. Abubakar, Yuming Feng, A. Ibrahim","doi":"10.1109/ICIST55546.2022.9926859","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926859","url":null,"abstract":"In this article, inspired by the overwhelming suc-cesses recorded by the inertial effect on existing iterative methods, we propose an inertial projection conjugate gradient (CG) method for solving nonlinear monotone operator equations with convex constraints. As a starting point, the inertial step is added to an existing CG method called PCG with the aim of speeding up its convergence. Under appropriate assumptions, the global convergence of the method is established. Numerical results are reported and in comparison with the PCG method, the effect of the inertial term is clearly seen as the propose approach outperforms the PCG method based on all metrics considered. This is evident that the inertial term has really performed its duty as expected.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131195108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a neurodynamic approach for a class of convex-concave minimax problems. First, variational inequalities are given, serving as the necessary and sufficient conditions for the desired saddle point of the underlying objective function. Next, based on the variational inequalities, a neurodynamic approach is designed for the minimax problems. Taking advantage of a proper Lyapunov function, the stability of the state solution of the proposed neurodynamic approach is guaranteed. Furthermore, the proposed neurodynamic approach is able to solve the non-quadratic convex-concave minimax problem exponentially. Compared with the existing researches for the quadratic minimax problem, the proposed neurodynamic approach has wider scope of applications to some extent. Finally, a numerical experiment is provided to show the effectiveness of the proposed neurodynamic approach.
{"title":"A Neurodynamic Approach for a Class of Convex-concave Minimax Problems","authors":"Zehua Xie, Xinrui Jiang, Sitian Qin, Jiqiang Feng, Shengbing Xu","doi":"10.1109/ICIST55546.2022.9926965","DOIUrl":"https://doi.org/10.1109/ICIST55546.2022.9926965","url":null,"abstract":"This paper presents a neurodynamic approach for a class of convex-concave minimax problems. First, variational inequalities are given, serving as the necessary and sufficient conditions for the desired saddle point of the underlying objective function. Next, based on the variational inequalities, a neurodynamic approach is designed for the minimax problems. Taking advantage of a proper Lyapunov function, the stability of the state solution of the proposed neurodynamic approach is guaranteed. Furthermore, the proposed neurodynamic approach is able to solve the non-quadratic convex-concave minimax problem exponentially. Compared with the existing researches for the quadratic minimax problem, the proposed neurodynamic approach has wider scope of applications to some extent. Finally, a numerical experiment is provided to show the effectiveness of the proposed neurodynamic approach.","PeriodicalId":211213,"journal":{"name":"2022 12th International Conference on Information Science and Technology (ICIST)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126091182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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