Ad Hoc network traffic prediction based on the Elman neural network

Ding, Yuanming; Gao, Jiayao; Wang, Xue

doi:10.2991/icmmita-15.2015.312

Cited by 2 publications

(5 citation statements)

References 3 publications

(2 reference statements)

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“…The Elman neural network and BP neural network have the same multilayer feed‐forward topology . The difference is that the Elman neural network has a function of undertaking layer, which plays the role of delayed memory so that the Elman network has the ability to adapt to the incident characteristics, which can directly reflect the dynamic process characteristics . The Elman neural network is generally divided into four layers: input layer, hidden layer (intermediate layer), receiver layer, and output layer, as shown in Fig.…”

Section: Processing Strategymentioning

confidence: 99%

“…The Elman neural network is generally divided into four layers: input layer, hidden layer (intermediate layer), receiver layer, and output layer, as shown in Fig. . The input layer unit only acts as a signal transmission, and the output layer unit acts as a linear weighting.…”

Section: Processing Strategymentioning

confidence: 99%

“…The transfer function of the hidden layer unit can be linear or nonlinear, and the receiver layer is used to memorize the output value of the hidden layer before the element and feedback to the input of the network. It can be regarded as a one‐step delay operator .…”

Section: Processing Strategymentioning

confidence: 99%

“…Elman neural network also uses the BP algorithm to modify the weights and learning index function using the error square sum function :

E (ω) = \sum_{k = 1}^{m} {([], Y_{k} ((), ω) - \overset{Y_{K}}{true} ((), ω))}^{2}

which is the target output vector.…”

Section: Processing Strategymentioning

confidence: 99%

“…SVM was originally developed for solving classification problems in which data need to be divided into two or more different categories and has been used to solve the regression problem (Ren yi, Yuhui) . Using SVM to solve the regression problem is called support vector machine regression, which has been widely used in regression problems such as wind energy and solar energy prediction .…”

Section: Processing Strategymentioning

confidence: 99%

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Prediction of wind power generation base on neural network in consideration of the fault time

Cao

Shi

et al. 2019

IEEJ Transactions Elec Engng

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Along with the rapid increase in wind power penetration into the power grid, wind power generation predicting is becoming increasingly important to power system operators and electricity market participants. However, the random nature of the wind power would increase the uncertainty of power systems. The influencing factor is one of the most important factors in the quality of wind power prediction. In order to obtain a higher prediction accuracy, a two-stage prediction method combined with meteorological factor and fault time is proposed. In the first stage, we present a detailed review of fault time of wind power predicting with machine-learning methods and compare ten different models based on different influencing factors, and then, the second stage is combined with the predicted results of first stage, coupled with meteorological factors for the final wind power prediction. The results show good prediction accuracy in operation data of one wind farm from the Hu-Nan province with different machine-learning algorithms.

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Section: Processing Strategymentioning

confidence: 99%

Section: Processing Strategymentioning

confidence: 99%

Section: Processing Strategymentioning

confidence: 99%

“…Elman neural network also uses the BP algorithm to modify the weights and learning index function using the error square sum function :

E (ω) = \sum_{k = 1}^{m} {([], Y_{k} ((), ω) - \overset{Y_{K}}{true} ((), ω))}^{2}

which is the target output vector.…”

Section: Processing Strategymentioning

confidence: 99%

Section: Processing Strategymentioning

confidence: 99%

See 3 more Smart Citations

Prediction of wind power generation base on neural network in consideration of the fault time

Cao

Shi

et al. 2019

IEEJ Transactions Elec Engng

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Application of MBR Membrane Flux Prediction Based on Elman Neural Network

Li¹,

Wang²

2018

dtetr

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In the field of MBR sewage treatment, membrane fouling affects the performance of MBR process. The direct consequence of it is the decline in membrane flux. The prediction of MBR flux is of great significance to the application of MBR. Because the neural network can arbitrarily approximate any continuous function with arbitrary precision, and it can establish a complex nonlinear relationship between input and output model, it is widely used in the field of prediction. We use a feedback type Elman neural network to model the MBR membrane flux, and use this model to predict membrane flux and evaluate the degree of membrane fouling. The results show that the prediction results of membrane flux obtained by Elman neural network prediction model are higher than BP neural network model. The prediction model has a guiding effect on the design and application of MBR system.

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Ad Hoc network traffic prediction based on the Elman neural network

Cited by 2 publications

References 3 publications

Prediction of wind power generation base on neural network in consideration of the fault time

Prediction of wind power generation base on neural network in consideration of the fault time

Application of MBR Membrane Flux Prediction Based on Elman Neural Network

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