A stacked sparse auto-encoder and back propagation network model for sensory event detection via a flexible ECoG

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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Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

Advances in Mechanical Engineering ◽

10.1177/1687814020984687 ◽

2020 ◽

Vol 12 (12) ◽

pp. 168781402098468

Author(s):

Xianbin Du ◽

Youqun Zhao ◽

Yijiang Ma ◽

Hongxun Fu

Keyword(s):

Neural Network ◽

Network Model ◽

Adaptive Learning ◽

High Speed ◽

Back Propagation ◽

Lateral Force ◽

Learning Ability ◽

Vehicle Performance ◽

Neural Network Theory ◽

Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

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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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