<title>Two-dimensional invariant pattern recognition using a back-propagation network improved by distributed associative memory</title>

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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Back propagation network based prediction on map-reduce platforms

Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems - DATA '19 ◽

10.1145/3368691.3368696 ◽

2019 ◽

Author(s):

Sandhya N ◽

Philip Samuel ◽

Mariamma Chacko

Keyword(s):

Back Propagation ◽

Map Reduce ◽

Propagation Network

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Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.64 ◽

1995 ◽

Vol 3 (3) ◽

pp. 133-142 ◽

Cited By ~ 10

Author(s):

M. Hana ◽

W.F. McClure ◽

T.B. Whitaker ◽

M. White ◽

D.R. Bahler

Keyword(s):

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Near Infrared ◽

Back Propagation ◽

Linear Network ◽

Data Set ◽

Input Layer ◽

Propagation Network ◽

Better Than

Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

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