This paper presents a new method to estimate the time of important earthquakes in Hormozgan region with magnitude greater than 5.5 based on the Radial Basis Function （RBF） Neural Network （NN） models. Input vector ...This paper presents a new method to estimate the time of important earthquakes in Hormozgan region with magnitude greater than 5.5 based on the Radial Basis Function （RBF） Neural Network （NN） models. Input vector to the network is composed of different seisrnicity rates between main events that are calculated in convenient and reliable way to create optimized training methods. It helps network with a limited number of training data to estimation. It is common for earthquakes modeling by data-driven methods in this case. In addition, the proposed method is combined with Rosenberg cluster method to remove aftershocks events from the history of catalog for NN to better process the data. The results show that created RBF model successfully estimates the interevent times between large and sequence earthquakes that can be used as a tool to predict earthquake, so that comparison with other NN structure, for example Multi- Layer Perceptron （MLP） NN, reveals the superiority of the proposed method. Because of superiority proposed method has higher accuracy, lower costs and simpler network structure.展开更多
This paper studies the distributed consensus control for linear multi-agent systems under discontinuous communication and control updating.A fully distributed event-triggered adaptive control protocol with strictly po...This paper studies the distributed consensus control for linear multi-agent systems under discontinuous communication and control updating.A fully distributed event-triggered adaptive control protocol with strictly positive minimum interevent time(MIET)guarantees is proposed.First,an event-triggered distributed adaptive control law without using prior global information of network topologies is presented,which achieves asymptotic consensus via discrete control updating and intermittent communication.Then,a hybrid adaptive event-triggering scheme with an internal timer is designed that is activated only when the timer decreases to zero from a specified positive value.Under the proposed triggering scheme,not only Zeno behavior is excluded but also a strictly positive MIET between any two consecutive events is guaranteed,which facilitates the physical implementation.In contrast to the existing related results,the proposed fully distributed protocol only needs low-frequency communication and control updating,while ensuring the strictly positive MIET property.Finally,a simulation example is given to illustrate the effectiveness of the theoretical results.展开更多
文摘This paper presents a new method to estimate the time of important earthquakes in Hormozgan region with magnitude greater than 5.5 based on the Radial Basis Function （RBF） Neural Network （NN） models. Input vector to the network is composed of different seisrnicity rates between main events that are calculated in convenient and reliable way to create optimized training methods. It helps network with a limited number of training data to estimation. It is common for earthquakes modeling by data-driven methods in this case. In addition, the proposed method is combined with Rosenberg cluster method to remove aftershocks events from the history of catalog for NN to better process the data. The results show that created RBF model successfully estimates the interevent times between large and sequence earthquakes that can be used as a tool to predict earthquake, so that comparison with other NN structure, for example Multi- Layer Perceptron （MLP） NN, reveals the superiority of the proposed method. Because of superiority proposed method has higher accuracy, lower costs and simpler network structure.
基金supported by the National Key Research and Development Project under Grant No.2020YFC1512503the National Natural Science Foundation of China under Grant No.61991414+1 种基金Chongqing Natural Science Foundation under Grant No.CSTB2023NSCQ-JQX0018Beijing Natural Science Foundation under Grant No.L221005。
文摘This paper studies the distributed consensus control for linear multi-agent systems under discontinuous communication and control updating.A fully distributed event-triggered adaptive control protocol with strictly positive minimum interevent time(MIET)guarantees is proposed.First,an event-triggered distributed adaptive control law without using prior global information of network topologies is presented,which achieves asymptotic consensus via discrete control updating and intermittent communication.Then,a hybrid adaptive event-triggering scheme with an internal timer is designed that is activated only when the timer decreases to zero from a specified positive value.Under the proposed triggering scheme,not only Zeno behavior is excluded but also a strictly positive MIET between any two consecutive events is guaranteed,which facilitates the physical implementation.In contrast to the existing related results,the proposed fully distributed protocol only needs low-frequency communication and control updating,while ensuring the strictly positive MIET property.Finally,a simulation example is given to illustrate the effectiveness of the theoretical results.
