scholarly journals ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning

2021 ◽  
Author(s):  
Chaofan Chen ◽  
Xiaoshan Yang ◽  
Changsheng Xu ◽  
Xuhui Huang ◽  
Zhe Ma
Keyword(s):  
Knowledge Propagation ◽  
Propagation Network

Multi-stage Knowledge Propagation Network for Recommendation

2021 ◽  
pp. 253-264
Author(s):  
Feng Xue ◽  
Wenjie Zhou ◽  
Zikun Hong ◽  
Kang Liu
Keyword(s):  
Multi Stage ◽  
Knowledge Propagation ◽  
Propagation Network

scholarly journals Knowledge Evolution of Complex Agent Networks

2018 ◽  
Vol 173 ◽  
pp. 03050
Author(s):  
Zhao Yeqing
Keyword(s):  
Social Network ◽  
Network Model ◽  
Knowledge Network ◽  
Obvious Effect ◽  
Complex Agent ◽  
The Social ◽  
Knowledge Propagation ◽  
Knowledge Agents ◽  
Propagation Network ◽  
Agent Networks

In order to study the interaction between structure change of social network and knowledge propagation, this paper proposes a complex agent network model to discover the inner rule and restraining factors of the knowledge diffusion in the network system. The agents take advantage of different social radius to form acquaintance networks based on the theory of social circles in the knowledge propagation network model, and the dynamic evolution process of knowledge network is realized according the defined rules of knowledge communication. Simulation results show that this model based on social circle theory can better realize the characteristics of the actual social network than the traditional network model established before, at the same time the social radius of knowledge agent for knowledge dissemination in knowledge network has the obvious effect. It can narrow the knowledge gap for the knowledge agents in social network and good social relation network can be developed.

scholarly journals Knowledge Evolution of Complex Agent Networks

2018 ◽  
Vol 176 ◽  
pp. 03007
Author(s):  
Zhao Yeqing
Keyword(s):  
Social Network ◽  
Network Model ◽  
Knowledge Network ◽  
Obvious Effect ◽  
Complex Agent ◽  
The Social ◽  
Knowledge Propagation ◽  
Knowledge Agents ◽  
Propagation Network ◽  
Agent Networks

In order to study the interaction between structure change of social network and knowledge propagation, this paper proposes a complex agent network model to discover the inner rule and restraining factors of the knowledge diffusion in the network system. The agents take advantage of different social radius to form acquaintance networks based on the theory of social circles in the knowledge propagation network model, and the dynamic evolution process of knowledge network is realized according the defined rules of knowledge communication. Simulation results show that this model based on social circle theory can better realize the characteristics of the actual social network than the traditional network model established before, at the same time the social radius of knowledge agent for knowledge dissemination in knowledge network has the obvious effect. It can narrow the knowledge gap for the knowledge agents in social network and good social relation network can be developed.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data

1995 ◽  
Vol 3 (3) ◽  
pp. 133-142 ◽  
Author(s):  
M. Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M. White ◽  
D.R. Bahler
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Near Infrared ◽  
Back Propagation ◽  
Linear Network ◽  
Data Set ◽  
Input Layer ◽  
Propagation Network ◽  
Better Than

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

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