Many real systems are known to interact with one another, forming networks of networks (NONs). Plenty of attention has been poured into the research on the robustness in NONs in the past decade. Previous studies focus...
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Many real systems are known to interact with one another, forming networks of networks (NONs). Plenty of attention has been poured into the research on the robustness in NONs in the past decade. Previous studies focus on undirected networks, or directed networks under random attacks. While many real networks are directed and how networks of directed networks (NODNs) respond to targeted attacks remains unknown. We thus develop a general analytical tool for analyzing the robustness of NODNs under two kinds of targeted attacks: degree-based attacks and in-degree (out-degree)-based attacks. The analytical tool can perfectly predict the sizes of the final giant strongly connected components and the phase transitions on the NODNs in response to targeted attacks. By applying the tool to synthesis networks, we find that a quadruple point intersected by four different phase regions could appear in the random regular NODNs. To the best of our knowledge, it is the first time that a quadruple point is found in the studies of complex networks. In addition, we find triple points intersected by three phases in networks of directed scale-free networks, and critical points that connect two phases in networks of directed Erdos-Renyi networks. The discovery of these tipping points could help understand network robustness and enable better design of networked systems.
This paper presents a novel approach to compute DCT-I, DCT-III, and DCT-IV. By using a modular mapping and truncating, DCTs are approximated by linear sums of discrete moments computed fast only through additions. Thi...
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作者：
Zhang, LupingXu, FeiHuazhong Univ Sci & Technol
Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Minist China Wuhan 430074 Hubei Peoples R China
Homogenous spiking neural P systems (HSNP systems) are a class of neuron-inspired computing models, where each neuron contains an identical set of rules. It remains open how to design universal asynchronous HSNP syste...
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Homogenous spiking neural P systems (HSNP systems) are a class of neuron-inspired computing models, where each neuron contains an identical set of rules. It remains open how to design universal asynchronous HSNP systems. In this work, we introduce local rule synchronization into asynchronous HSNP systems, and such systems are abbreviated as AHSNPR systems. Specifically, a family of the rule sets is specified;a rule in a specified rule set is applied synchronously with all the other rules in the same set, and a rule not in any specified rule set is applied asynchronously. We investigate the number generating power of AHSNPR systems. It is proved that general and unbounded AHSNPR systems are universal, and bounded AHSNPR systems are only able to characterize the semilinear sets of numbers achieving the corresponding properties of decidability and closure. The results show that the local rule synchronization is useful in constructing universal asynchronous HSNP systems. (C) 2022 Elsevier B.V. All rights reserved.
The look-ahead is a forbidding condition formalized by a set of forbidden rules that are checked after all assignment of objects to rules are done. The look-ahead mode can decrease the inherent non-determinism of P sy...
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Landing footprint of an entry vehicle provides critical information for mission planning. Conventional methods calculate it through solving a family of multi-constraints optimal control problems. It is difficult to so...
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A method based on multi-agents and ANN (Artificial Neural Network) was proposed to solve the pursuit-evasion task in continuous time-varying environment. According to this method, several autonomous agents with 8 circ...
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To drive a single joint of rehabilitation robotic arm, we propose a new PM-TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS). Unlike the traditional agonist/antagonist PM actuator, the PM is arr...
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ISBN:
(纸本)9780955529337
To drive a single joint of rehabilitation robotic arm, we propose a new PM-TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS). Unlike the traditional agonist/antagonist PM actuator, the PM is arranged in appropriate place as agonist and the torsion spring provides opposing torque as antagonist in the proposed actuator. The I-DOF and 2-DOF rehabilitation robotic arm models are derived considering the PM-TS dynamic model. To realize a high-accurate trajectory tracking control of the robotic arms, an intelligent PID controller based on an Echo State Neural Network (ESN) is proposed, where the ESN state is updated by the online Recursive Least Square (RLS) algorithm. Simulation results demonstrate the validity of PM-TS actuators. The performance of RLS-ESN based PID controller is found more satisfactory than conventional PID controller in our study.
In this paper, stabilization for a class of Takagi-Sugeno (T-S) fuzzy memristive neural networks (FMNNs) with mixed time delays is investigated. By virtue of theories of differential equations with discontinuous right...
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In this paper, stabilization for a class of Takagi-Sugeno (T-S) fuzzy memristive neural networks (FMNNs) with mixed time delays is investigated. By virtue of theories of differential equations with discontinuous right-hand sides, inequality techniques, and the comparison method, an algebraic criterion is derived to stabilize the addressed FMNNs with bounded discrete and distributed time delays via a designed fuzzy state feedback controller in Filippov's sense. The result can be reinforced to stabilize FMNNs with unbounded discrete time delays. Meanwhile, exponential stabilization of FMNNs with bounded discrete time delays and unbounded continuously distributed delays is also discussed. FMNNs in this study are general since fuzzy logics and hybrid time delays are all considered, and the obtained conditions enhance and extend some existing ones. Finally, four numerical simulations are carried out to substantiate the efficiency and merits of developed theoretical results.
Synchronization and pinning control of complex networks is to regulate the agents' behavior and improve network performance. In this article, we review some recent developments in pinning control. Stability algori...
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The sampled-data based event-triggered strategy for consensus of leader-following multi-agent systems with the input-delay and non-linear dynamics is studied. A novel event-triggered transmission strategy is proposed,...
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The sampled-data based event-triggered strategy for consensus of leader-following multi-agent systems with the input-delay and non-linear dynamics is studied. A novel event-triggered transmission strategy is proposed, where the controller of each agent updates and the sampled-data of each agent transmits to its neighbours' only at the agent's own triggering time instants. The measurement errors are defined based on the state information of the local neighbourhood and leader to design the event triggering scheme, and this scheme reduces the number of event triggering times, the controller updating and communications between agents. Moreover, in the proposed event triggered control strategy, the Zeno-behaviour is avoided. The control protocol and event triggering scheme can be simply designed by solving an LMI condition to achieve leader-following consensus asymptotically. The effectiveness of the proposed results is demonstrated via a numerical example.
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