A Novel Neural Network Based Modeling for Control of NOx Emission in Power Plant

2014 ◽  
Vol 643 ◽  
pp. 385-390
Author(s):  
Ping Kang Li ◽  
Rui Pan ◽  
Chen Chen
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Model Identification ◽  
Training Algorithm ◽  
Optimal Prediction ◽  
Prediction Formula ◽  
Steady State Model ◽  
Identification Technique ◽  
One Step ◽  
And Training

A novel neural network based modeling for non-linear model identification technique is proposed. It combines a nonlinear steady state model with a linear one, to describe the disturbance and dynamics in the coal-fired power plant. The modeling and training algorithm is used to develop a model of nitrogen oxides (NOx) emitted from the process where one-step ahead optimal prediction formula are developed. Two cases show that the resulting model provides a better prediction of NOx and fitting capabilities.

scholarly journals Solving Mixed Volterra - Fredholm Integral Equation (MVFIE) by Designing Neural Network

2019 ◽  
Vol 16 (1) ◽  
pp. 0116
Author(s):  
Al-Saif Et al.
Keyword(s):  
Neural Network ◽  
Integral Equation ◽  
Analytic Solution ◽  
Fredholm Integral Equations ◽  
Training Algorithm ◽  
Output Unit ◽  
Feed Forward Neural Network ◽  
Levenberg Marquardt ◽  
Hidden Layer ◽  
And Training

       In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.                                  

Research on Hyperspectral Data Classification Based on Quantum Counter Propagation Neural Network

2012 ◽  
Vol 546-547 ◽  
pp. 1377-1381
Author(s):  
Xuan Hou
Keyword(s):  
Neural Network ◽  
Quantum Theory ◽  
Topological Structure ◽  
Learning Algorithm ◽  
Data Classification ◽  
Hyperspectral Data ◽  
Training Algorithm ◽  
Training Process ◽  
Traditional Classification ◽  
And Training

It proposes the model and learning algorithm of Quantum Counter Propagation Neural Network and applies which in hyperspectral data classification as well. On one hand, introducing quantum theory into the structure or training process of Counter Propagation Neural Network with regard to improving structure and capacity of Classical Neural Network, enhancing learning and generalization ability of it. On the other hand, establishing a new topological structure and training algorithm of Quantum Counter Propagation Neural Network by the means of quoting the thought, concept and principles of quantum theory directly. To complete the experiment of hyperspectral data classification with three ways and the result shows that effects of Quantum Counter Propagation Neural Network is superior to the traditional classification.

scholarly journals Architecture and Training Algorithm of The Adjusting Neural Network for Accurate Model Tuning.

1995 ◽  
Vol 115 (4) ◽  
pp. 461-469 ◽  
Author(s):  
Yoichi Sugita ◽  
Masahiro Kayama ◽  
Yasuo Morooka ◽  
Yutaka Saito
Keyword(s):  
Neural Network ◽  
Training Algorithm ◽  
Accurate Model ◽  
Model Tuning ◽  
And Training

Development of a Prediction Algorithm for Column Shortening in High-Rise Buildings Using a Neural Network

2007 ◽  
Vol 348-349 ◽  
pp. 901-904
Author(s):  
Won Jik Yang ◽  
Waon Ho Yi
Keyword(s):  
Neural Network ◽  
Training Data ◽  
Prediction Algorithm ◽  
Training Methods ◽  
Training Algorithm ◽  
High Rise ◽  
Concrete Members ◽  
Polynomial Fit ◽  
Neural Network Algorithm ◽  
And Training

The objective of this study is to formulate and evaluate a new training algorithm of Neural Network to predict the inelastic shortening of reinforced concrete members using the column shortening data of high-rise buildings. The new training algorithm of Neural Network for the prediction of column shortening focuses on component of input data and training methods. The validity is examined by training and prediction process based on column shortening measuring data of high-rise buildings. The polynomial fit line of measuring data is used as the training data instead of measuring data. The result shows that the new Neural Network algorithm proposed in this study successfully predicts column shortening of high-rise buildings.

Research on the NNARX Model Identification of Hydroelectric Unit Based on Improved L-M Algorithm

2013 ◽  
Vol 871 ◽  
pp. 304-309 ◽  
Author(s):  
Zhi Huai Xiao ◽  
Zhou Peng An ◽  
Shu Qing Wang ◽  
Shi Qi Zeng
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Controller Design ◽  
Model Identification ◽  
Hydroelectric Power Plant ◽  
Identification Accuracy ◽  
Hydroelectric Power ◽  
Generalization Ability ◽  
Marquardt Algorithm ◽  
High Recognition Accuracy

Its hard to use traditional ways to set up a hydroelectric power plant model due to its complex, time-varying and nonlinear characteristics. This article uses the neural network autoregressive with exogenous input (NNARX) to identify and model hydro-turbine generating unit which is the key in the hydroelectric power plant modeling. The random guide vain signal is used to train NNARX in this paper and the other two working conditions are used to check its generalization ability. In order to improve identification accuracy, generalization performance and training speeds, an improved Levenberg-Marquardt algorithm is proposed in this article which is based on the L-M algorithm that widely used in artificial neural network weights adjustment. Simulation results indicate that NNARX model with improved L-M algorithm can reach high recognition accuracy and have good generalization ability. It can provide a good simulation model for intelligent controller design in the future.

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