Abstract This paper is concerned with adaptive learning algorithms for Bayesian network classifiers in a prequential (on-line) learning scenario. In this scenario, new data is available over time. An efficient supervised learning algorithm must be able to improve its predictive accuracy by incorporating the incoming data, while optimizing the cost of updating. However, if the process is not str...

Adaptive networks are a novel class of dynamical networks whose topologies and states coevolve over similar time scales. Many real-world complex systems (physical, biological and social ones) can be modeled as adaptive networks. In this short paper, we introduce fundamental concepts and unique properties of adaptive networks through a brief, noncomprehensive review of recent literature. We also...

Putting health theories, research and knowledge into practice is a challenge referred to as the knowledge-toaction gap. Knowledge translation (KT), and its related concepts of knowledge mobilization, implementation science and research impact, emerged to mitigate this gap. While the social interaction view of KT has gained currency, scholars have not easily made a link between KT and the concep...

social capital is one of the most important criteria related to social network resilience. in trust networks as the most important factor of social capital in adaptive governance, sensitivity to disorders and irregularities is considerable and resiliency improvement and compatibility capacity depends on trust level in network. this research is applied in zangooi village, south khorasan province...

This contribution studies the impact of adaptive quadrature amplitude modulation (AQAM) on network performance when applied to a cellular network, using adaptive antennas in conjunction with both fixed channel allocation (FCA) and locally distributed dynamic channel allocation (DCA) schemes. The performance advantages of using adaptive modulation are investigated in terms of the overall network...

Adaptive networks are complex networks with nontrivial topological features and connection patterns between their elements which are neither purely regular nor purely random. Their applications are in sociology, biology, physics, genetics, epidemiology, chemistry, ecology, materials science, the traditional Internet and the emerging Internet-of-Things. For example, their applications in sociolo...

Neural network adaptive filters are mainly used for the interference cancellation techniques. The gradient based design methods are well developed for the design of neural network adaptive filter but they converge to local minima. This paper describes the global optimization interference cancelling techniques for adaptive filtering of interferences in the corrupted signal. The system is designe...

The aim of this paper was to present an optimized method in order to use maximum capacity of the photovoltaic panels. In this regard, we presented a method for the maximum power point tracking in the photovoltaic systems by using the neural networks and adaptive controller. In the proposed system, we estimated an error by using neural network. If this error is lower than the allowable systems e...

Two local adaptive strategies for the synchronization of complex networks are discussed in this paper. One, termed as vertex-based, uses local adaptive coupling gains at each node in the network. The other, named edge-based, associates to each edge in the network an adaptive coupling gain, determined solely on the basis of local information. The global asymptotic stability of the synchronous ev...

Much recent research has focused on using situations rather than individual pieces of context as a means to trigger adaptive system behaviour. While current research on situations emphasises their representation and composition, they do not provide an approach on how to organise and identify their occurrences efficiently. This paper describes how lattice theory can be utilised to organise situa...