This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential decay of the influence of past input samp...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential decay of the influence of past input samples on the current model output. This is done by penalizing the sensitivity of the NARX model simulated output with respect to the past inputs. This promotes the stability of the estimated models and improves the obtained model quality. The effectiveness of the approach is demonstrated through a simulation example, where a neural network NARX model is identified with this novel method. Moreover, it is shown that the proposed regularization approach improves the model accuracy in terms of simulation error performance compared to that of other regularization methods and model classes.
This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks based upon non-Euclidean contraction theory. The basic idea is to cast the robustness ...
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The cooperative output regulation problem has been extensively studied on the basis of the distributed observer approach. However, the majority of the existing research assumes that the dynamics is known previously. T...
The cooperative output regulation problem has been extensively studied on the basis of the distributed observer approach. However, the majority of the existing research assumes that the dynamics is known previously. To remove this condition, the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is unknown. First, a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external inputs. Second, the unknown regulator equations are solved using the iterative recurrent neural network approach. Third, the state-based data-driven distributed control law is synthesized to solve the problem. The optimal gains are derived by solving convex optimization problems using input and state data. Finally, a numerical example is presented to verify the feasibility of the proposed framework.
A new 3D memristor system is constructed based on VB17 system. The basic dynamics of the system are analyzed by means of bifurcation analysis, phase orbit diagram and Lie index. Based on the idea of bias and the intro...
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A new 3D memristor system is constructed based on VB17 system. The basic dynamics of the system are analyzed by means of bifurcation analysis, phase orbit diagram and Lie index. Based on the idea of bias and the introduction of trigonometric functions, the attractor self-propagation phenomenon is generated in the system, which proves that the newly proposed memristor system has super many steady states. In addition, the system has the phenomenon of bias lift. Finally, the realization of the theory is proved by designing the circuit of 3D memory chaotic system. Multisim simulation verifies the validity the validity of the numerical analysis.
With the development of communication and sensing technology, it has become possible to monitor the operating status of the power grid system through a series of sensors. However, malicious adversaries may launch data...
With the development of communication and sensing technology, it has become possible to monitor the operating status of the power grid system through a series of sensors. However, malicious adversaries may launch data integrity attacks to compromise the measurements of certain sensors, causing the grid monitoring system to fail to grasp the correct system operating status. To solve the NP-hard suspicious sensor selection problem, this paper proposes an efficient attack detection scheduling algorithm, the Particle Swarm Optimization algorithm based on historical information directional guidance (HIDG-based PSO algorithm). The proposed algorithm is utilized with its unique evolutionary mechanism, which reduces the computational power requirements for sensor selection. For the problem of uncertainty in evolutionary algorithms, this paper uses historical information to guide the selection of suspicious sensors at the current moment. The simulation results show that the proposed algorithm can efficiently select suspicious sensors, which will greatly improve the efficiency of attack detection and ensure the security of information fusion of the power grid monitoring system.
The carbon neutrality target and the carbon emission problem caused by the ever-increasing energy demand necessitate the extensive use of renewable energy (RES) generation technologies. Multi-energy microgrid (MEMG) b...
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This work presents a novel regularization method for the identification of Nonlinear Autoregressive eXogenous (NARX) models. The regularization method promotes the exponential decay of the influence of past input samp...
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To increase energy safety when working with electrical devices (electronic equipment), a number of devices are used, such as a circuit breaker, RCCB circuit breaker, fuse links, relays and others. All of those compone...
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Truck drivers are required to stop and rest with a certain regularity according to the driving and rest time regulations, also called Hours-of-Service (HoS) regulations. This paper studies the problem of optimally for...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
Truck drivers are required to stop and rest with a certain regularity according to the driving and rest time regulations, also called Hours-of-Service (HoS) regulations. This paper studies the problem of optimally forming platoons when considering realistic HoS regulations. In our problem, trucks have fixed routes in a transportation network and can wait at hubs along their routes to form platoons with others while fulfilling the driving and rest time constraints. We propose a distributed decision-making scheme where each truck controls its waiting times at hubs based on the predicted schedules of others. The decoupling of trucks’ decision-makings contributes to an approximate dynamic programming approach for platoon coordination under HoS regulations. Finally, we perform a simulation over the Swedish road network with one thousand trucks to evaluate the achieved platooning benefits under the HoS regulations in the European Union (EU). The simulation results show that, on average, trucks drive in platoons for 37% of their routes if each truck is allowed to be delayed for 5 % of its total travel time. If trucks are not allowed to be delayed, they drive in platoons for 12 % of their routes.
This paper studies stability of switched systems that are composed of a mixture of stable and unstable modes with multiple equilibria. The main results of this paper include some sufficient conditions concerning set c...
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