An artificial neural network approach for routing in distributed computer networks

Pierre, Samuel; Said, Hassane; Probst, Wilfried

doi:10.1016/s0952-1976(00)00059-2

Cited by 11 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is a mathematical model, which can perform parallel processing and nonlinear transformation by simulating the way a biological neural network processes information [5]. The objective of the ANN is to model certain mechanisms of the brain to realize specific function.…”

Section: Methodology Of Bpnmentioning

confidence: 99%

Intelligent Structural Rating System Based on Backpropagation Network

Chen

Ren

Liu

2013

Journal of Aircraft

View full text Add to dashboard Cite

Section: Methodology Of Bpnmentioning

confidence: 99%

Intelligent Structural Rating System Based on Backpropagation Network

Chen

Ren

Liu

2013

Journal of Aircraft

View full text Add to dashboard Cite

“…The approximation capabilities of the multilayer perceptron (MLP) architecture make it a popular choice for modeling cases employed by chemical, petroleum and natural gas engineers 10 . MLPs are composed of the input layer, hidden layer(s) and output layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Continuous Prediction of the Gas Dew Point Temperature for the Prevention of the Foaming Phenomenon in Acid Gas Removal Units Using Artificial Intelligence Models

Rohani¹,

Jazayeri-Rad²,

Behbahani³

2017

IJCIS

View full text Add to dashboard Cite

Acid gas removal (AGR) units are widely used to remove CO 2 and H 2 S from sour gas streams in natural gas processing. When foaming occurs in an AGR system, the efficiency of the process extremely decreases. In this paper, a novel approach is suggested to regularly predict the gas dew point temperature (GDPT) in order to anticipate the foaming conditions. Prediction of GDPT is advantageous because the conventional methods of measuring GDPT such as: (i) using a chilled mirror device is time consuming; and (ii) the use of gas chromatograph for composition determination combined with the equation-of-state calculations involve column retention time and is expensive. New hybrid modeling algorithms based on the artificial neural network (ANN) combined with either the imperialist competitive algorithm (ICA) or particle swarm optimization (PSO) are employed to model the process. The models can then be used to prevent the foaming phenomenon. The proposed algorithms combine the local searching ability of ANN with the global searching abilities of ICA and PSO. ICA and PSO are used to optimize the initial weights of the neural networks. The resulting ICA-ANN and PSO-ANN combined algorithms are then applied to model the occurrence of foaming in the AGR plant based on a simulation data set acquired from the 6 th refinery of the south Pars gas complex in Iran. The performances of the ICA-ANN, PSO-ANN and conventional ANN models are then compared against each other. It was found that the accuracies of the ICA-ANN and PSO-ANN models are better than that of the conventional ANN model. In addition, the PSO-ANN model outperformed the ICA-ANN model.

show abstract

“…However, to help rapid convergence of the network, small perturbations are applied to the initial inputs of network. Initial random noise helps to break symmetry which may caused by paths with same reliability or by the possibility of having two or more high reliable path set, while preventing it from adopting an undesirable state 20,47 . At the start and based on our simulation results, U i 's are chosen randomly such that -0.0005<U i <0.0005.…”

Section: Hopfield Network Initializationmentioning

confidence: 99%

PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks

Sheikhan¹,

Hemmati²

2012

IJCIS

View full text Add to dashboard Cite

Mobile ad-hoc network (MANET) is a dynamic collection of mobile computers without the need for any existing infrastructure. Nodes in a MANET act as hosts and routers. Designing of robust routing algorithms for MANETs is a challenging task. Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network. However, selecting an optimal multipath is an NP-complete problem. In this paper, Hopfield neural network (HNN) which its parameters are optimized by particle swarm optimization (PSO) algorithm is proposed as multipath routing algorithm. Link expiration time (LET) between each two nodes is used as the link reliability estimation metric. This approach can find either node-disjoint or link-disjoint paths in singlephase route discovery. Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA) in terms of the path set reliability and number of paths in the set.

show abstract

An artificial neural network approach for routing in distributed computer networks

Cited by 11 publications

References 20 publications

Intelligent Structural Rating System Based on Backpropagation Network

Intelligent Structural Rating System Based on Backpropagation Network

Continuous Prediction of the Gas Dew Point Temperature for the Prevention of the Foaming Phenomenon in Acid Gas Removal Units Using Artificial Intelligence Models

PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks

Contact Info

Product

Resources

About