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As a kind of kernel methods, kernel recursive least squares has attracted wide attention in the research of time series online prediction. It has low computational complexity and updates in the shape of recursive increment. However, with the increase of data size, computational complexity of calculating kernel inverse matrix will raise. In addition, in the process of online prediction, it cannot accommodate...
Identification of different communities in large weighted networks is of crucial importance since it helps to uncover priori unknown functional modules such as topics in information networks or cyber-communities in social networks. However, the typical size of networks, such as social network services or World Wide Web, now counts in millions of nodes and is computationally complex. This urgently...
Energy harvesting (EH) enabled relaying has attracted considerable attentions as an effective way to prolong the operation time of energy-constrained networks and extend coverage beside desired survivability and rate of transmission. In most existing literatures, the Harvest-Store-Use (HSU) model is utilized to describe the energy flow behavior of the EH system. However, the half-duplex (HD) constraint...
With the increasingly widespread adoption of cloud computing and tenants' growing needs for large-scale data processing, cluster scheduling frameworks (e.g. MapReduce, Spark, etc.) have emerged as important programming models that works for distributed and parallel computing on cloud systems. While several recent researches proposed some solutions to optimize the MapReduce-like scheduler, they hardly...
Photo recommendation in photo-sharing social networks like Flickr is an important problem. Collaborative filtering is very popular, which assumes each item has the same weight for recommendation. In practice some items are representatives for a class of items and therefore are more important for recommendation. In this paper, we model the importance for items by examining sentiment from the general...
Flight landing optimization at the terminal area is an ongoing challenge for air traffic controllers. The current schedule scheme is first-come-first-served (FCFS). There are studies focusing on how to minimize the total cost or maximize the throughput. These schemes are short of fairness consideration. In this paper, we start from a real recent example to show that a lack of consideration of long-term...
Source address filtering is used as an important mechanism to prevent malicious traffic. Currently, most networks store filters in hardware such as TCAM, which has limited capacity, high power consumption and high cost. Although software can accommodate large number of filters, it needs multiple accesses to memory on the border router, which bears much more additional burden than other routers. In...
The cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task...
In recent years, the core-net routing table, e.g., Forwarding Information Base (FIB), is growing at an alarming speed and this has become a major concern for Internet Service Providers. One effective solution for this routing scalability problem, which requires only upgrades on individual routers, is FIB aggregation. Intrinsically, IP prefixes with numerical prefix matching and the same next hop can...
On context of time-of-use electricity price market, this paper mainly proposes a feasible PHEV charging mechanism for peak load management, the grid's peak load regulation model and PHEV terminal cost model are separately established. In accordance with the mechanism, this paper also puts forward an optimization algorithm based on dynamic estimate interpolation concept. At last, an example is simulated...
Adaptive dynamic surface control is presented for a class of nonaffine pure-feedback systems with unknown time-delay using neural networks. The problem of “explosion of complexity” in the traditional backstepping algorithm is avoided using dynamic surface control (DSC). The effects of unknown time-delays are eliminated by using appropriate Lyapunov-Krasovskii functionals in the design procedure. The...
An adaptive neural network control (ANNC) is proposed for a class of strict-feedback uncertain nonlinear systems with unknown system nonlinearities and unknown virtual control gain nonlinearities. Combining the dynamic surface control (DSC) technique with minimal-learning-parameters (MLP) algorithm, a systematic procedure for synthesis of ANNC is developed based on the universal approximation of neural...
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