Using Artificial Neural Back-Propagation Network Model to Detect the Outliers in Semiconductor Manufacturing Machines

2014 ◽  
pp. 240-249
Author(s):  
Keng-Chieh Yang ◽  
Chia-Hui Huang ◽  
Conna Yang ◽  
Pei-Yao Chao ◽  
Po-Hong Shih
Keyword(s):  
Network Model ◽  
Semiconductor Manufacturing ◽  
Back Propagation ◽  
Artificial Neural ◽  
Propagation Network

Applying Artificial Neural Networks: Part II. Using near Infrared Data to Classify Tobacco Types and Identify Native Grown Tobacco

1997 ◽  
Vol 5 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Maha Hana ◽  
W.F. McClure ◽  
T.B. Whitaker ◽  
M.W. White ◽  
D.R. Bahler
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Near Infrared ◽  
Back Propagation ◽  
Calibration Model ◽  
Linear Network ◽  
Burley Tobacco ◽  
The Usa ◽  
Artificial Neural ◽  
Propagation Network

Classification of flue-cured and Burley tobacco types with artificial neural networks (ANNs) were studied. Burley tobacco was further classified as either grown in the USA or grown outside the USA. The input data were in the form of near infrared (NIR) spectra, each spectrum containing 19 points. The number of flue-cured and Burley samples were 654 and 959, respectively. The number of native and non-native tobacco samples were 266 and 267, respectively. The models selected for this research were a quadratic classifier, a back-propagation network and a linear network. The results of the calibration model and the true performance for classifying tobacco species were (100%, 100%), (99.38%, 99.39%) and (95.19%, 99.26%) for the quadratic classifier, back-propagation network and linear network, respectively. The identification of native tobacco and its true performance were (100%, 100%) using a quadratic classifier, (89.12%, 88.46%) using a back-propagation network and (80.68%, 79.62%) using a linear network.

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.

Mechanics Analysis of Long Jump by Means of the Theory of Artificial Neural Networks

2014 ◽  
Vol 1014 ◽  
pp. 106-109
Author(s):  
Qun Long Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Excellent Performance ◽  
Mechanics Analysis ◽  
Long Jump ◽  
Artificial Neural ◽  
Run Up ◽  
Propagation Network ◽  
Mechanics Characteristics

Takeoff is extremely important to long jump. The paper analyzes the mechanics characteristics of takeoff for long jump by means of the theory of neural network. It firstly discusses some importantly influencing factors for long jump in theory. On the basis of description of the theory of Artificial Neural Network, the back propagation network is applied to model the long jump. The results show that an excellent performance of long jump is depend on a rapid run-up speed and the rhythm of the final two steps.

Investigating the role of weight update functions in developing artificial neural network modeling of retention times of furan and phenol derivatives

2013 ◽  
Vol 91 (4) ◽  
pp. 255-262
Author(s):  
Zahra Garkani-Nejad ◽  
Behzad Ahmadi-Roudi
Keyword(s):  
Conjugate Gradient ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Phenol Derivatives ◽  
Levenberg Marquardt ◽  
One Step ◽  
Artificial Neural ◽  
Comparison Of The Results ◽  
Propagation Network

A quantitative structure−retention relationship study has been carried out on the retention times of 63 furan and phenol derivatives using artificial neural networks (ANNs). First, a large number of descriptors were calculated using HyperChem, Mopac, and Dragon softwares. Then, a suitable number of these descriptors were selected using a multiple linear regression technique. This paper focuses on investigating the role of weight update functions in developing ANNs. Therefore, selected descriptors were used as inputs for ANNs with six different weight update functions including the Levenberg−Marquardt back-propagation network, scaled conjugate gradient back-propagation network, conjugate gradient back-propagation with Powell−Beale restarts network, one-step secant back-propagation network, resilient back-propagation network, and gradient descent with momentum back-propagation network. Comparison of the results indicates that the Levenberg−Marquardt back-propagation network has better predictive power than the other methods.

Seismic Behavior of Concrete Filled Circular Steel Tubular Columns Based on Artificial Neural Network

2012 ◽  
Vol 502 ◽  
pp. 189-192
Author(s):  
Hua Wei ◽  
Yu Du ◽  
Hai Jun Wang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
High Strength ◽  
Steel Tube ◽  
Structure Design ◽  
Seismic Properties ◽  
Artificial Neural ◽  
Propagation Network

Artificial neural network (ANN) is self-adaptability, fault toleration and fuzziness. It is suitable to solve the seismic properties of high strength reinforced concrete columns with concrete filled steel tube core (HRCCFT). A three-layer back-propagation network model is build up to study the seismic properties of HRCCFT. The model is trained according to 30 sets of experimental data. The network convergence is fast. The model is verified by 8 groups of experimental data, the results show the predicted values of displacement ductility are in good agreement with test values. The precision of model is better than that of formula from other reference. This method is good enough to be used as an auxiliary method for structure design.

Study on the Forecast Model of Water Quantity on the Basis of BP Artificial Neural Network

2013 ◽  
Vol 475-476 ◽  
pp. 188-191
Author(s):  
Xiao Bin Ding
Keyword(s):  
Water Flow ◽  
Yellow River ◽  
Back Propagation ◽  
Forecast Model ◽  
Bp Network ◽  
Bp Artificial Neural Network ◽  
Functional Fitting ◽  
Artificial Neural ◽  
Propagation Network ◽  
Bp Model

Back Propagation network, Widely used in automatic control, image recognition, hydrological forecasting and water quality evaluation, etc., as one of the Artificial Neural Networks, has stronger property of mapping, classification, functional fitting. This article takes the water flow of Lanzhou section of Yellow river as example by use of BP model to predict the water flow. It is well proved that BP network model can reach the purposes of early warning and forecasting.

scholarly journals ESTIMATING STOCK RETURNS USING ARTIFICIAL NEURAL NETWORKS: AN EXPERIMENTAL DESIGN WITH AN EVIDENCE FROM IRAQ STOCK EXCHANGE

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Mohammed H Adnan ◽  
Mustafa Muneer Isma’eel
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Markets ◽  
Stock Returns ◽  
Stock Exchange ◽  
Back Propagation ◽  
Error Back Propagation ◽  
Artificial Neural ◽  
Propagation Network

The research aims to estimate stock returns using artificial neural networks and to test the performance of the Error Back Propagation network, for its effectiveness and accuracy in predicting the returns of stocks and their potential in the field of financial markets and to rationalize investor decisions. A sample of companies listed on the Iraq Stock Exchange was selected with (38) stock for a time series spanning (120) months for the years (2010_2019). The research found that there is a weakness in the network of Error Back Propagation training and the identification of data patterns of stock returns as individual inputs feeding the network due to the high fluctuation in the rates of returns leads to variation in proportions and in different directions, negatively and positively.

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