scholarly journals General Regression Neural Network versus Back Propagation Neural Network for Prediction of Reheater and Super Heater Sprays in Thermal Power Plants

2020 ◽  
Vol 9 (4S2) ◽  
pp. 90-93
Keyword(s):  
Neural Network ◽  
Power Plants ◽  
Thermal Power ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
General Regression Neural Network ◽  
Thermal Power Plants ◽  
Neural Network Models ◽  
Super Heater ◽  
General Regression

Neural Network models are used for Reheater and Super heater spray prediction in Thermal Power Plants. This paper makes a comparative study of the General Regression Neural Network (GRNN) model versus the Back propagation Neural Network (BPNN) model for the quality and accuracy of prediction of Reheater and Super heater Sprays in Thermal Power Plants. It proves that GRNN is better and gives more stable prediction within range; the glitches between the predicted and actual values being less in number as well as value

ARMA Neural Networks for Predicting DGPS Pseudorange Correction

2004 ◽  
Vol 57 (2) ◽  
pp. 275-286 ◽  
Author(s):  
Dah-Jing Jwo ◽  
Tai-Shen Lee ◽  
Ying-Wei Tseng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
General Regression Neural Network ◽  
Performance Study ◽  
Comparative Performance ◽  
The Neural Network ◽  
Moving Averaging ◽  
General Regression

In this paper, the Auto-Regressive Moving-Averaging (ARMA) neural networks (NNs) will be incorporated for predicting the differential Global Positioning System (DGPS) pseudorange correction (PRC) information. The neural network is employed to realize the time-varying ARMA implementation. Online training for real-time prediction of the PRC enhances the continuity of service on the differential correction signals and therefore improves the positioning accuracy. When the PRC signal is lost, the ARMA neural network predicted PRC would temporarily provide correction data with very good accuracy. Simulation is conducted for evaluating the ARMA NN based DGPS PRC prediction accuracy. A comparative performance study based on two types of ARMA neural networks, i.e. Back-propagation Neural Network (BPNN) and General Regression Neural Network (GRNN), will be provided.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

scholarly journals Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Aminmohammad Saberian ◽  
H. Hizam ◽  
M. A. M. Radzi ◽  
M. Z. A. Ab Kadir ◽  
Maryam Mirzaei
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
General Regression Neural Network ◽  
Maximum Temperature ◽  
Photovoltaic Panel ◽  
The Neural Networks ◽  
Artificial Neural ◽  
General Regression

This paper presents a solar power modelling method using artificial neural networks (ANNs). Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

Comparison of BP and GRNN Algorithm for Factory Monitoring

2011 ◽  
Vol 52-54 ◽  
pp. 2105-2110 ◽  
Author(s):  
Ing Jiunn Su ◽  
Chia Chih Tsai ◽  
Wen Tsai Sung
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Predictive Ability ◽  
Network Models ◽  
General Regression Neural Network ◽  
Data Sets ◽  
Artificial Neural ◽  
Propagation Network ◽  
General Regression ◽  
Monitoring Area

Artificial neural networks (ANNs) are one of the most recently explored advanced technologies which show promise in the factory monitoring area. This paper focuses on two particular network models, back-propagation network (BPN) and general regression neural network (GRNN). The prediction accuracy of these two models is evaluated using a practical application situation in a monitor factory. GRNN emerged as a variant of the artificial neural network. Its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. According the simulation results we can show that GRNN is an effective way to considerably improve the predictive ability of BPN.

Estimation of NOx Emissions in Thermal Power Plants Using Neural Networks

2001 ◽  
Vol 123 (2) ◽  
pp. 465-471 ◽  
Author(s):  
G. Ferretti ◽  
L. Piroddi
Keyword(s):  
Neural Network ◽  
Power Plants ◽  
Thermal Power ◽  
Three Dimensional ◽  
Estimation Procedure ◽  
Thermal Power Plants ◽  
Nox Emissions ◽  
Generation Process ◽  
Trained Neural Network ◽  
Local Nature

In this paper a neural network-based strategy is proposed for the estimation of the NOx emissions in thermal power plants, fed with both oil and methane fuel. A detailed analysis based on a three-dimensional simulator of the combustion chamber has pointed out the local nature of the NOx generation process, which takes place mainly in the burners’ zones. This fact has been suitably exploited in developing a compound estimation procedure, which makes use of the trained neural network together with a classical one-dimensional model of the chamber. Two different learning procedures have been investigated, both based on the external inputs to the burners and a suitable mean cell temperature, while using local and global NOx flow rates as learning signals, respectively. The approach has been assessed with respect to both simulated and experimental data.

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