A Neural Network-Based On-line Monitoring Model of Process Mean and Variance Shifts

2010 ◽  
Author(s):  
Bin Wu ◽  
Jian-bo Yu
Keyword(s):  
Neural Network ◽  
Process Mean ◽  
Monitoring Model ◽  
On Line ◽  
Mean And Variance

The Artificial Neural Network On-Line Monitoring Model of Boiler Efficiency

2013 ◽  
Vol 773 ◽  
pp. 239-243
Author(s):  
Xin Dai ◽  
Bin Yang ◽  
Ya Feng Zhong ◽  
Yong Hong Guo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monitoring Model ◽  
Wide Range ◽  
Precise Analysis ◽  
Boiler Efficiency ◽  
On Line ◽  
Artificial Neural ◽  
The Heat Balance ◽  
Selection Of

When adjusting the borler combustion, the borler efficiency need to be constantly monitored.The traditional method of calculating boiler efficiency is complex.Based on the heat balance method,the main factors of influencing boiler efficiency was analysed deeply and the artificial neural network on-line monitoring model of boiler efficiency was established to predict boiler efficiency accurately and constantly in this paper. After precise analysis and tracking, the input variable for the artificial neural network on-line monitoring model of boiler efficiency was selected, so as to avoid larger error caused by the rough selection of input variable in the previous artificial neural network. At last,based on a 600MW boiler,the borler efficiency was predicted in this paper.we can easily know from the prediction result that the artificial neural network on-line monitoring model of boiler efficiency can predict the boiler efficiency accurately and constantly at a wide range condition.

Simultaneous Process Mean and Variance Monitoring Using Wavelet Transform and Probabilistic Neural Network

2012 ◽  
Vol 157-158 ◽  
pp. 11-15 ◽  
Author(s):  
Shao Xiong Wu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Input Data ◽  
Probabilistic Neural Network ◽  
Process Mean ◽  
Mean And Variance ◽  
Simultaneous Process ◽  
The Mean ◽  
Simulation Results

A real-time WPNN-based model was present for the simultaneous recognition of both mean and variance CCPs. In the modeling of structure for patterns recognition, the combined wavelet transform with probabilistic neural network (WPNN) was proposed. Input data was decomposed by wavelet transform into several detail coefficients and approximations. The approximation obtained and energy of every lever detail coefficients was for the input of PNN. The simulation results shows that it can recognize each pattern of the mean and variance CCPs accurately, which can be used in simultaneous process mean and variance monitoring.

scholarly journals AN ON-LINE NON-LEPTONIC NEURAL TRIGGER APPLIED TO AN EXPERIMENT LOOKING FOR BEAUTY

1994 ◽  
Vol 05 (05) ◽  
pp. 863-870
Author(s):  
C. BALDANZA ◽  
F. BISI ◽  
A. COTTA-RAMUSINO ◽  
I. D’ANTONE ◽  
L. MALFERRARI ◽  
...  
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Enrichment Factor ◽  
Data Stream ◽  
Particle Production ◽  
Event Sample ◽  
On Line

Results from a non-leptonic neural-network trigger hosted by experiment WA92, looking for beauty particle production from 350 GeV π− on a Cu target, are presented. The neural trigger has been used to send on a special data stream (the Fast Stream) events to be analyzed with high priority. The non-leptonic signature uses microvertex detector data and was devised so as to enrich the fraction of events containing C3 secondary vertices (i.e, vertices having three tracks whith sum of electric charges equal to +1 or -1). The neural trigger module consists of a VME crate hosting two ETANN analog neural chips from Intel. The neural trigger operated for two continuous weeks during the WA92 1993 run. For an acceptance of 15% for C3 events, the neural trigger yields a C3 enrichment factor of 6.6–7.1 (depending on the event sample considered), which multiplied by that already provided by the standard non-leptonic trigger leads to a global C3 enrichment factor of ≈150. In the event sample selected by the neural trigger for the Fast Stream, 1 every ≈7 events contains a C3 vertex. The response time of the neural trigger module is 5.8 μs.

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