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Global Synchronization of General Delayed Dynamical networks

Chinese Physics Letters 2007年第7期24卷 1869-1872页

作者：李智 Laboratory of Nonlinear Science Institute of Mathematics Fudan University Shanghai 200433 Department of Automatic Control Engineering Xidian University~ PO Box 136 Xi'an 710071

Global synchronization of general delayed dynamical networks with linear coupling are investigated. A sufficient condition for the global synchronization is obtained by using the linear matrix inequality and introducing a reference state. This condition is simply given based on the maximum nonzero eigenvalue of the network coupling matrix. Moreover, we show how to construct the coupling matrix to guarantee global synchronization of network, which is very convenient to use. A two-dimension system with delay as a dynamical node in network with global coupling is finally presented to verify the theoretical results of the proposed global synchronization scheme.

关键词： neural-networks

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Synchronizing Complex networks by an Adaptive Adjustment Mechanism

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Chinese Physics Letters 2006年第11期23卷 2909-2912页

作者：卜寿亮张有维汪秉宏 Department of Physics Dalian University of Technology Dalian 116024 Department of Modern Physics and Nonlinear Science Center University of Science and Technology of China Hefei 230026 Shanghai Academy of System Science Shanghai 200093

We propose an adaptive adjustment mechanism for synchronizing complex networks, in particular for sociological or/and biological systems. We do not take it for granted that a dynamical system is put on isolated nodes and they are coupled with each other by one （or more） variable（s）, as employed in most previous models. As a replacement, we suppose that each node may have any finite number of possible states, and their evolutions with time are determined by their nearest-neighbouring （or even second-nearest-neighbouring, etc） nodes in an adaptive adjustment mechanism. It is found that synchronization can be achieved for almost all connected networks and that the scale-free property can evidently improve the synchronizing speed.

关键词： SMALL-WORLD networks COUPLED SYSTEMS neural-networks CHAOS EMERGENCE DYNAMICS FEEDBACK NEURONS

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Amplified Signal Response by Neuronal Diversity on Complex networks

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Chinese Physics Letters 2008年第5期25卷 1591-1594页

作者：申传胜陈含爽张季谦 1Department of Educational Technology Anqing Teachers College Anqing 246011 College of Physics and Electronic Information Anhui Normal University Wuhu 241000

The effect of diversity on dynamics of coupled FitzHugh-Nagumo neurons on complex networks is numerically investigated, where each neuron is subjected to an external subthreshold signal. With the diversity the network is a mixture of excitable and oscillatory neurons, and the diversity is determined by the variance of the system＇s parameter. The complex network is constructed by randomly adding long-range connections （shortcuts） on a nearest-neighbouring coupled one-dimensional chain. Numerical results show that external signals are maximally magnified at an intermediate value of the diversity, as in the case of well-known stochastic resonance, burthermore, the effects of the number of shortcuts and coupled strength on the diversity-induced phenomena are also discussed. These findings exhibit that the diversity may play a constructive role in response to external signal, and highlight the importance of the diversity on such complex networks.

关键词： SMALL-WORLD networks STOCHASTIC RESONANCE COHERENCE RESONANCE neural-networks SYNCHRONIZATION SYSTEMS INFORMATION MECHANISM DISORDER NOISE

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Statistical Models for Long-range Forecasting of Southwest Monsoon Rainfall over India Using Step Wise Regression and neural Network

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Atmospheric and Climate Sciences 2012年第3期2卷 322-336页

作者： Ashok Kumar D. S. Pai J. V. Singh Ranjeet Singh D. R. Sikka 43 Ritu Apartments A-4 New Delhi India National Center for Medium Range Weather Forecasting Noida India National Climate Centre India Meteorological Department Shivajinagar Pune India

The long-range forecasts (LRF) based on statistical methods for southwest monsoon rainfall over India (ISMR) has been issued by the India Meteorological Department (IMD) for more than 100 years. Many statistical and dynamical models including the operational models of IMD failed to predict the operational models of IMD failed to predict the deficient monsoon years 2002 and 2004 on the earlier occasions and so had happened for monsoon 2009. In this paper a brief of the recent methods being followed for LRF that is 8-parameter and 10-parameter power regression models used from 2003 to 2006 and new statistical ensemble forecasting system are explained. Then the new three stage procedure is explained. In this the most pertinent predictors are selected from the set of all the potential predictors for April, June and July models. The model equations are developed by using the linear regression and neural network techniques based upon training set of the 43 years of data from 1958 to 2000. The skill of the models is evaluated based upon the validation set of 11 years of data from 2001 to 2011, which has shown the high skill on the validation data set. It can be inferred that these models have the potential to provide a prediction of ISMR, which would significantly improve the operational forecast.

关键词： Monsoon ISMR LRF Step-Wise Regression neural-networks

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