Titlebook: Consensus and Synchronization in Complex Networks; Ljupco Kocarev Book 2013 Springer-Verlag Berlin Heidelberg 2013 Consensus in distribute

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書目名稱Consensus and Synchronization in Complex Networks影響因子(影響力)




書目名稱Consensus and Synchronization in Complex Networks影響因子(影響力)學(xué)科排名




書目名稱Consensus and Synchronization in Complex Networks網(wǎng)絡(luò)公開度




書目名稱Consensus and Synchronization in Complex Networks網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Consensus and Synchronization in Complex Networks被引頻次




書目名稱Consensus and Synchronization in Complex Networks被引頻次學(xué)科排名




書目名稱Consensus and Synchronization in Complex Networks年度引用




書目名稱Consensus and Synchronization in Complex Networks年度引用學(xué)科排名




書目名稱Consensus and Synchronization in Complex Networks讀者反饋




書目名稱Consensus and Synchronization in Complex Networks讀者反饋學(xué)科排名

STANT

Control of Networks of Coupled Dynamical Systems,he individual systems to the forcing signal. By considering the control signal as the state of a virtual dynamical system, this problem can be studied as a synchronization problem. The main focus of this chapter is to link the effectiveness of such control to various properties of the underlying gra

Jubilation

羽飾

Consensus of Networked Multi-agent Systems with Delays and Fractional-Order Dynamics,nput delay, communication delay, fractional-order dynamics and directed information flow on the consensus behavior of networked multi-agent systems are systematically studied. We find that consensus is very robust against communication delays in both integer-order systems and fractional-order system

Foreknowledge

Factual

Enhancing Synchronizability of Complex Networks via Optimization,e usually tackled with deterministic approaches. On the other hand, metaheuristic algorithms have received a lot of attentions and have been successful applied for many difficult problems. In this chapter, it is to showcase how to use the metaheuristic algorithms to provide better solutions to the o

Factual

Synchronization-Based Parameter Estimation in Chaotic Dynamical Systems,s output of the system. The method drives the model’s parameters by a set of proper parameter update rules to the true values of the parameters of the modeled system. The theory on how to construct this parameter update rules is given along with simple demonstrations with the Lorenz and R?ssler syst

Pulmonary-Veins

Data Assimilation as Artificial Perception and Supermodeling as Artificial Consciousness, (observed data) in one direction. Though investigated most thoroughly in meteorology, the task of data assimilation arises in any situation where a predictive computational model is updated in run time by new observations of the target system, including the case where that model is a perceiving bio

改進(jìn)

Supermodeling Dynamics and Learning Mechanisms,s of the observed climate, each with its own strengths and weaknesses. In the current multi-model ensemble approach model simulations are combined a posteriori. Recently, it has been proposed to dynamically combine the models and so construct one supermodel. The supermodel parameters are learned fro

Decibel