scholarly journals Soft-sensing of NOx content in power Station based on BP Neural Network, RBF Neural Network and PCA-RBF Neural Network

2018 ◽  
Vol 392 (6) ◽  
pp. 062180
Author(s):  
Peitao Hu ◽  
Liu Changliang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Power Station ◽  
Soft Sensing

Application of BP Neural Network into the Kow of Chemical Contaminants

2010 ◽  
Vol 121-122 ◽  
pp. 574-578
Author(s):  
Hui Yu Jiang ◽  
Min Dong ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Partition Coefficient ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Rbf Neural Network ◽  
Physical Parameters ◽  
Bp Network ◽  
Chemical Contaminants ◽  
Water Partition Coefficient ◽  
Molecular Bond

The octanol / water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol / water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the BP network to predict, the correlation coefficient has achieved 0.998, the prediction error in the permission scope.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

Intelligent Identification of Flow Regime Based on Improved BP and IMFs

2013 ◽  
Vol 816-817 ◽  
pp. 471-474
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Flow Regime ◽  
Bp Neural Network ◽  
Flow Regimes ◽  
Characteristic Vector ◽  
Soft Sensing ◽  
Mode Decomposition ◽  
Training Samples ◽  
The Difference ◽  
Regime Characteristic

Aimed at the characteristic of nonlinear and non-stationary of pressure drop, in this article a flow regime identification soft sensing method using Hilbert-Huang transformation combined with improved BP neural network is put forward. The method analyzes the intrinsic mode function (IMFs) obtained after the empirical mode decomposition (EMD), then extracts IMF energy as the characteristic vector of an improved BP neural network with self-adapted learning ratio. Learning form training samples, the network could accomplish the objective identification of the unknown flow regimes. The simulated results showed that the flow regime characteristic vector which was obtained by IMFs could reflect the difference between various flow regimes and the recognition possibility of the network could reached up to about 95 percent. This study provided a new way to identify flow regime by soft sensing.

scholarly journals Research on power prediction of photovoltaic power station based on similar hour and LM-BP neural network

2021 ◽  
Vol 252 ◽  
pp. 01056
Author(s):  
Qiang Zhang ◽  
Gang Liu ◽  
Xiangzhong Wei
Keyword(s):  
Neural Network ◽  
Output Power ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Weather Conditions ◽  
Power Prediction ◽  
Power Station ◽  
Short Term ◽  
Factors Affecting ◽  
Photovoltaic Power

Aiming to solve the problem of low precision of traditional photovoltaic power forecast method under abrupt weather conditions. In this paper, a high-precision photovoltaic power prediction method based on similarity time and LM-BP neural network is proposed. Firstly, the factors affecting the output power of photovoltaic power station are analyzed, and the short-term output power model of photovoltaic power station is established based on similar day and LM-BP neural network. Then, from the perspective of model training efficiency and prediction accuracy, the deficiencies in the short-term power prediction of photovoltaic power stations based on similar days and LM-BP algorithm are analyzed. Secondly, the prediction model of LM-BP neural network based on similar hours is established. Finally, Jiaxing photovoltaic power station is taken as an example for simulation verification. The simulation results show that the proposed method has high accuracy in predicting photovoltaic power under abrupt weather conditions.

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