A traffic identification based on PSO-RBF neural network in peer-to-peer network

Chen, Yong; Ji, Huiqin; Liu, Huanlin; Sun, Longzhao

doi:10.1504/ijcse.2016.078444

“…This indicates the solution corresponding to must locate in the subspace spanned . So is equivalent to (15) and there will be factor to make (16) Synthesizing formula ( 12), ( 15) and ( 16), the following formula can be obtained:…”

Section: Data Dimensionality Reduction By Kpcamentioning

confidence: 99%

“…Reviewing these literatures, in the method research aspect, the black-box model are employed widely, e.g., the ARX model and feed-forward ANN. The ARX model is theoretically suitable for dealing with linear problems, but HVAC&R system has obvious nonlinear characteristics; ANN has strong nonlinear processing ability, so it is more suitable for fault diagnosis of HVAC&R. The most used feed-forward ANN mainly includes back-propagation neural network (BPNN) and RBFNN, the RBFNN has higher generalization ability and learning efficiency than t BPNN, and it has no local minimum problem existed in BPNN [16,17]. Although RBFNN has been applied relatively successfully in fault diagnosis, the conventional RBFNN models mainly have two problems in the training.…”

Section: Introductionmentioning

confidence: 99%

Compound fault diagnosis for cooling dehumidifier based on RBF neural network improved by kernel principle component analysis and adaptive genetic algorithm

Gao

¹

,

Ma

²

,

Sheng

³

2022

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Developing fault diagnosis for the cooling dehumidifier is very important for improving the equipment reliability and saving energy consumption. This paper mainly studies and explores the compound fault diagnosis for the cooling dehumidifier. Firstly, the dehumidifier data acquisition system is built, which can be applied to the data acquisition, work status simulation, and fault diagnosis. Secondly, a compound fault diagnosis model based on radial basis function neural network (RBFNN)improved by kernel principle component analysis (KPCA) and adaptive genetic algorithm (AGA) is proposed. Aiming at the problems that the selection of RBF width depends on expert knowledge, the network structure scale is large or the training speed is slow in the conventional RBFNN models, on the one hand, AGA and K-means clustering algorithm are employed to automatically optimize the RBF width, the number of hidden layer neurons and the neuron centers, which guarantees the model has small structure and fast computing speed on the premise of sufficient output precision; on the other hand, KPCA is used to reduce the dimension of the model input data, which not only effectively extracts the nonlinear features, but also further simplifies the network structure. Finally, the proposed method is validated and compared with the conventional models. The results show that this proposed model can not only be effectively applied to the dehumidifier compound fault diagnosis, but also has prominent application advantages.

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“…Liu et al (2016) use Bayesian probabilistic inference to forecast the load information in multi-tenant service environment and proposed a service migration method for load balancing. Chen et al (2016) propose a novel classification method of peer-to-peer network traffic identification based on machine learning for balancing the link load and improving QoS. Multipath routing schemes with load balancing has been widely applied in IP network, which can be classified into traditional IP-based and multiprotocol label switching (MPLS)-based.…”

Section: Related Workmentioning

confidence: 99%

Load-balanced overlay recovery scheme with delay minimisation

Tian

¹

,

Yang

²

2019

IJHPCN

1

0

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Recovery from a link or node failure in the internet is often subjected to seconds or minutes of routing convergence, during which certain end-to-end connections may experience seconds or minutes of outage. According to this problem, existing approaches reroute the data traffic to a pre-defined backup path to detour the failed components. However, the maintenance of backup path increases the significant bandwidth expenditure. On the other hand, the diverted traffic may cause congestion on the backup path if it is not carefully split over multiple paths according to their available capacity. In this paper, we propose an efficient recovery scheme by using one-hop overlay multipath source routing, which is a post-failure recovery method. Once a failure happens, multiple one-hop overlay paths are constructed by selecting strategically multiple relay nodes, and the affected traffic is diverted to these paths in a well-balanced manner. We formulate the traffic allocation problem as a tractable linear programming (LP) optimisation problem, whose goal is to minimise the worse-case network congestion ratio. Simulations based on a real ISP network and a synthetic internet topology show that our scheme can effectively balance link utilisation dramatically and improve the reliability of network.

show abstract

Load-balanced overlay recovery scheme with delay minimisation

Tian

¹

,

Yang

²

2019

IJHPCN

0

View full text Add to dashboard Cite

Recovery from a link or node failure in the internet is often subjected to seconds or minutes of routing convergence, during which certain end-to-end connections may experience seconds or minutes of outage. According to this problem, existing approaches reroute the data traffic to a pre-defined backup path to detour the failed components. However, the maintenance of backup path increases the significant bandwidth expenditure. On the other hand, the diverted traffic may cause congestion on the backup path if it is not carefully split over multiple paths according to their available capacity. In this paper, we propose an efficient recovery scheme by using one-hop overlay multipath source routing, which is a post-failure recovery method. Once a failure happens, multiple one-hop overlay paths are constructed by selecting strategically multiple relay nodes, and the affected traffic is diverted to these paths in a well-balanced manner. We formulate the traffic allocation problem as a tractable linear programming (LP) optimisation problem, whose goal is to minimise the worse-case network congestion ratio. Simulations based on a real ISP network and a synthetic internet topology show that our scheme can effectively balance link utilisation dramatically and improve the reliability of network.

show abstract

A traffic identification based on PSO-RBF neural network in peer-to-peer network

Cited by 5 publications

References 13 publications

Compound fault diagnosis for cooling dehumidifier based on RBF neural network improved by kernel principle component analysis and adaptive genetic algorithm

Compound fault diagnosis for cooling dehumidifier based on RBF neural network improved by kernel principle component analysis and adaptive genetic algorithm

Load-balanced overlay recovery scheme with delay minimisation

Load-balanced overlay recovery scheme with delay minimisation

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