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Reconstruction of noise-driven nonlinear dynamic networks with some hidden nodes 被引量:1
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作者 Yang chen Chao Yang Zhang +2 位作者 tian yu chen Shi Hong Wang Gang Hu 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS CSCD 2017年第7期40-47,共8页
The problem of network reconstruction, particularly exploring unknown network structures by analyzing measurable output data from networks, has attracted significant interest in many interdisciplinary fields in recent... The problem of network reconstruction, particularly exploring unknown network structures by analyzing measurable output data from networks, has attracted significant interest in many interdisciplinary fields in recent times. In practice, networks may be very large, and data can often be measured for only some of the nodes in a network while data for other variables are bidden. It is thus crucial to be able to infer networks from partial data. In this article, we study the problem of noise-driven nonlinear networks with some hidden nodes. Various difficulties appear jointly: nonlinearity of network dynamics, the impact of strong noise, the complexity of interaction structures between network nodes, and missing data from certain hidden nodes. We propose using high-order correlation to treat nonlinearity and structural complexity, two-time correlation to decorrelate noise, and higher- order derivatives to overcome the difficulties of hidden nodes. A closed form of network reconstruction is derived, and numerical simulations confirm the theoretical predictions. 展开更多
关键词 network reconstruction hidden nodes noise nonlinear dynamics neuron networks
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