查看更多>>摘要：Concept drift is a common and important issue in streaming data analysis and mining. Thus far, many concept drift detection methods have been proposed but may not be able to identify the type of concept drift, which will result in some difficulties, such as extracting the wrong key information, inadequate model learning and poor detection efficiency. To solve these problems, a concept drift type identification method is proposed based on multi sliding windows (CDT_MSW). This method consists of three processes. During the first detection process, the drift position is detected by sliding the basic window forward. Then, in the growth process, the drift length is detected using the growth of the adjoint window, and the drift category is identified according to the drift length. Finally, during tracking process, the drift subcategory can be accurately identified according to the different tracking flow ratio curves generated during window tracking. Experimental results show that the proposed method can effectively identify the type of concept drift, accurately analyze the key information during online learning and improve the efficiency and generalization performance of streaming data analysis and mining. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：This study investigates the leader-follower time-varying output formation problem in heterogeneous multi-agent systems with an active leader subject to cyber-attacks. The leader is active, implying that it has a bounded but unknown control input to modify the behavior of followers in response to changes or threats and consequently avoid unexpected circumstances. A resilient observer with a cyber-attack compensation signal is first performed to estimate the leader's state and output for the followers, while ensuring that the impact of cyber-attacks in the communication network is compensated. A compensation signal, along with a feasibility condition, then acts on the followers to complete the specified time-varying formation offset. The transformation of the output leader-follower formation problem to the conventional tracking problem is then further realized by a state-space transformation method. Next, inhomogeneous algebraic Riccati equations are derived by learning the solution to the conventional tracking problem for which its optimization is guaranteed. Finally, the effectiveness and availability of the proposed method are demonstrated through a simulation example. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Traditional topic classification usually adopts the closed-world assumption that all the test topics have been seen in training. However, in open dynamic environments, the potential new topics may appear in testing due to the evolution of text data over time. Considering the uncertainty and multi-granularity of dynamic text data, such open topic classification needs to detect unseen topics by mining the boundary region continually, and incremen-tally update the previous models by knowledge accumulation. To address these challenge issues, this paper introduces a unified framework of three-way multi-granularity learning to open topic classification based on the fusion of three-way decision and granular comput-ing. First, we propose the multilevel granular structure of tasks from the temporal-spatial multi-granularity perspective. Then, we construct an adaptive decision boundary and use the centroids and the corresponding radius to discover unknowns by the reject option. Subsequently, we further explore the unknown topics by three-way enhanced clustering and the uncertain instances will be re-investigated in the next stage. Besides, we design a built-in knowledge base represented as the centroid of each topic to store the topic knowledge. Finally, the experiments are conducted to compare the performances of pro-posed models and the efficiency of knowledge accumulation with classic models. (c) 2021 Elsevier Inc. All rights reserved.
Zhang, ChunyanLiu, SiyuanWang, ZhijieWeissing, Franz J....
12页
查看更多>>摘要：The emergence and maintenance of cooperation is a popular topic in studies of information sciences and evolutionary game theory. In two-player iterated games, memory in terms of the outcome of previous interactions and the strategy choices of co-players are of great referential significance for subsequent strategy actions. It is generally recognized that there is no single simple and overarching strategy whereby one player X can unilaterally achieve a higher payoff than his opponent Y, irrespective of Y's strategy and response. In this paper, we demonstrate that such strategies do exist in diverse networked populations. More precisely, (i) such strategies can obtain a low payoff for the focal player, however, they also lead to an even lower payoff for that player's partner, in turn lowering benefits of the overall populations; (ii) they are capable of winning with a high probability against opponents with an unknown strategy; and (iii) they have a survival advantage and robust fitness in terms of evolutionary processes. We refer to these as the "self-bad, partner-worse" (SBPW) strategies. Results presented here add to previous studies on strategy evolution in the context of social dilemmas and hint at some insights concerning cooperation promotion mechanisms among networked populations. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：The main objective of the study is to investigate the Takagi-Sugeno fuzzy-model-based networked control system under the fuzzy event-triggered H-infinity control scheme. Instead of considering the conventional network transmission delay, the paper introduces the probability distribution based network transmission delay, which is more compatible with real time-simulations. Due to distributed transmission delay, the closed-loop network con-trol system has become distributed delay system. To make use of full information about the membership function, the Lyapunov function candidate is designed as fuzzy-membership-dependent Lyapunov function. A switching concept with respect to the rate of changes in membership functions is introduced to design the optimal control gain matrices for the considered system. The main advantage of the fuzzy-membership-dependent Lyapunov function is that it can ensure the less conservatism of the result without increasing the number of decision variables. To validate the effectiveness of the proposed approach, two kinds are real-time systems are taken into account, one is nonlinear mass-spring-damper model, followed by an industrial example, say, surface mounted permanent mag-net synchronous motor model. The corresponding simulation results show the superiority of the proposed results over the existing works. (C) 2021 Elsevier Inc. All rights reserved.
de Jesus Rubio, JoseAntonio Islas, MarcoOchoa, GenaroRicardo Cruz, David...
24页
查看更多>>摘要：In the neural network adaptation, the Newton method could find a minimum with its second-order partial derivatives, and convergent gradient steepest descent could assure its error convergence with its time-varying adaptation rates. In this article, the convergent Newton method is proposed as the combination of the Newton method and the convergent gradient steepest descent for the neural networks adaptation, where the convergent Newton method incorporates the second-order partial derivatives inside of the time-varying adaptation rates. Hence, the convergent Newton method could assure its error con-vergence and could find a minimum. Experiments show that the convergent Newton method obtains satisfactory results in the electric energy usage data prediction. (c) 2021 Elsevier Inc. All rights reserved.
Gonzalez-Diaz, YanirMartinez-Trinidad, Jose FcoCarrasco-Ochoa, Jesus A.Lazo-Cortes, Manuel S....
14页
查看更多>>摘要：ABSTR A C T In this paper, we introduce an algorithm for computing all the shortest reducts in a deci-sion system. The proposed algorithm is based on determining the size of the shortest reducts using a small super-reduct and some new pruning methods. Once the size of the shortest reduct is determined, all other reducts of the same size are found applying the new pruning methods. The results of our experiments using several synthetic and real -world decision systems show that the proposed algorithm is, in most cases, faster than the state of the art algorithms for computing all the shortest reducts reported in the literature. (c) 2021 Elsevier Inc. All rights reserved.
Sakr, Ahmed S.Plawiak, PawelTadeusiewicz, RyszardHammad, Mohamed...
17页
查看更多>>摘要：Recently, electrocardiogram (ECG) signals have received a high level of attention as a physiological signal in the field of biometrics. It has presented great possibilities for its strength against counterfeit. However, the ECG feature templates are irreplaceable, and a compromised template implies a permanent loss of identity. Therefore, several studies have been introduced biometric template protection techniques such as cancelable techniques to protect the original template in case it is stolen or lost. In this research, a cancelable ECG approach is proposed to protect the ECG feature template for human authentication. In our system, we first employed some image processing techniques for preprocessing the input ECG signals. Then, a deep transfer learning approach is employed to extract the deep ECG features. Later, the proposed cancelable approach based on DNA and amino acid is applied to protect the deep feature templates. Lastly, a Support Vector Machine (SVM) is employed for authentication. Extensive experiments on two commonly used datasets coupled with comprehensive theoretical analysis demonstrate the highest accuracy of the proposed system and the strong resilience of the system to various security and privacy attacks. Results show that the proposed cancelable method meets all requirements of cancelable biometrics such as irreversibility, revocability, and unlinkability. (c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
查看更多>>摘要：The main objective of the study is to investigate the Takagi-Sugeno fuzzy-model-based networked control system under the fuzzy event-triggered H-infinity control scheme. Instead of considering the conventional network transmission delay, the paper introduces the probability distribution based network transmission delay, which is more compatible with real time simulations. Due to distributed transmission delay, the closed-loop network control system has become distributed delay system. To make use of full information about the membership function, the Lyapunov function candidate is designed as fuzzy-membership dependent Lyapunov function. A switching concept with respect to the rate of changes in membership functions is introduced to design the optimal control gain matrices for the considered system. The main advantage of the fuzzy-membership-dependent Lyapunov function is that it can ensure the less conservatism of the result without increasing the number of decision variables. To validate the effectiveness of the proposed approach, two kinds are real-time systems are taken into account, one is nonlinear mass-spring-damper model, followed by an industrial example, say, surface mounted permanent magnet synchronous motor model. The corresponding simulation results show the superiority of the proposed results over the existing works. (C) 2021 Elsevier Inc. All rights reserved.
Zamfirache, Iuliu AlexandruPrecup, Radu-EmilRoman, Raul-CristianPetriu, Emil M....
14页
查看更多>>摘要：This paper presents a new Reinforcement Learning (RL)-based control approach that uses the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the Neural Networks (NNs). Due to an efficient tradeoff to exploration and exploitation, the GWO algorithm shows good results in NN training and solving complex optimization problems. The proposed approach is compared to the classical PI RL-based control approach using the Gradient Descent (GD) algorithm, and with the RL-based control approach which uses the metaheuristic Particle Swarm Optimization (PSO) algorithm. The experiments are conducted using a nonlinear servo system laboratory equipment. Each approach evaluated on how well it solves the optimal reference tracking control for an experimental servo system position control system. The policy NNs specific to all three approaches are implemented as state feedback with integrator controllers to remove the steady-state control errors and thus ensure the convergence of the objective function. Because of the random nature of metaheuristic algorithms, the experiments for GWO and PSO algorithms are run multiple times and the results are averaged before the conclusions are presented. The experimental results shows that for the control objective considered in this paper, the GWO algorithm represents a better solution compared to GD and PSO algorithms. (c) 2021 Elsevier Inc. All rights reserved.
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