By Guido Caldarelli, Michele Catanzaro
Networks are serious about many facets of daily life, from meals webs in ecology and the unfold of pandemics to social networking and public transportation. actually, essentially the most vital and regular usual structures and social phenomena are in accordance with a networked constitution.
It is most unlikely to appreciate the unfold of an endemic, a working laptop or computer virus, large-scale blackouts, or enormous extinctions with out making an allowance for the community constitution that underlies a majority of these phenomena.
In this Very brief creation, Guido Caldarelli and Michele Catanzaro speak about the character and diversity of networks, utilizing daily examples from society, expertise, nature, and heritage to light up the technology of community idea. The authors describe the ever-present position of networks, demonstrate how networks self-organize, clarify why the wealthy get richer, and speak about how networks can spontaneously cave in. They finish via highlighting how the findings of complicated community thought have very broad and critical purposes in genetics, ecology, communications, economics, and sociology.
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Foreign convention clever community and Intelligence in Networks (2IN97) French Ministry of Telecommunication, 20 street de Segur, Paris -France September 2-5, 1997 Organizer: IFIP WG 6. 7 -Intelligent Networks Sponsorship: IEEE, Alcatel, Ericsson, France Telecom, Nokia, Nordic Teleoperators, Siemens, Telecom Finland, Lab.
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Additional resources for Networks: A Very Short Introduction (Very Short Introductions)
Synchronization in General Complex Delayed Dynamical Networks. IEEE Trans. CAS-I 53 (2006) 733-744 12. : Stability Analysis of Delay Neural Networks with Impulsive Eﬀects. IEEE Trans. CAS-II 52 (2005) 517-521. 13. : Impulsive Control Theory. O. cn 1 2 Abstract. In this paper, we study the eﬀect of parameter mismatches on the fuzzy neural networks with impulses. Since it is impossible to make two non-identical neural networks complete synchronized, we study the synchronization of two neural networks in terms of quasi-synchronization.
043901. 15. : New Approach to Synchronization Analysis of Linearly Coupled Ordinary Diﬀerential Systems. Physica D 213 (2006) 214-230 16. : Synchronization in Chaotic systems. Physical Review Letters 64 (1990) 821-824. 17. : Synchronization in An Array of Linearly Coupled Networks with Time-varying Delay. Physica A, 366(2006) 197-211 18. Wu,C. : A Uniﬁed Framework for Synchronization and Control of Dynamical Systems. Int. J. Bifurcation Chaos, 4 (1994) 979-989 32 T. Huang and C. Li 19. : Adaptive Exponential Synchronization of Delayed Chaotic Networks.
Liang, P. Li, and Y. Yang above discussions, in this paper, the following nonlinearly coupled neural network model will be studied: dxi (t) dt N = −Cxi (t) + Af (xi (t)) + Bf (xi (t − τ )) + I(t) + Gij Df (xj (t)) (1) j=1 where i = 1, 2, . . , N and xi (t) = (xi1 (t), . . , xin (t))T is the state vector of the ith network at time t; C = diag(c1 , . . , cn ) > 0 denotes the rate with which the cell i resets its potential to the resting state when isolated from other cells and inputs; A and B are the weight matrix and the delayed weight matrix, respectively; activation function f (xi (t)) = (f1 (xi1 (t)), .
Networks: A Very Short Introduction (Very Short Introductions) by Guido Caldarelli, Michele Catanzaro