Laser spot detection-based computer interface system using autoassociative multilayer perceptron with input-to-output mapping-sensitive error back propagation learning algorithm

2011 ◽  
Vol 50 (8) ◽  
pp. 084302 ◽  
Author(s):  
Chanwoong Jung
Keyword(s):  
Multilayer Perceptron ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Computer Interface ◽  
Laser Spot ◽  
Error Back Propagation ◽  
Spot Detection ◽  
Output Mapping ◽  
Interface System

scholarly journals A three-stage learning algorithm for deep multilayer perceptron with effective weight initialisation based on sparse auto-encoder

2019 ◽  
Vol 8 (1) ◽  
pp. 41
Author(s):  
Maysa Ibrahem Almulla Khalaf ◽  
John Q Gan
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Multilayer Perceptron ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Error Back Propagation ◽  
Second Stage ◽  
Propagation Network

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures and values of learning parameters are determined through cross-validation, and test datasets unseen in the cross-validation are used to evaluate the performance of the DMLP trained using the three-stage learning algorithm. Experimental results show that the proposed method is effective in combating overfitting in training deep neural networks.

The TP Chromatic Aberration Estimation by Using Neural Networks

2011 ◽  
Vol 121-126 ◽  
pp. 4239-4243 ◽  
Author(s):  
Du Jou Huang ◽  
Yu Ju Chen ◽  
Huang Chu Huang ◽  
Yu An Lin ◽  
Rey Chue Hwang
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Physical Property ◽  
Chromatic Aberration ◽  
Neural Model ◽  
Artificial Intelligent ◽  
Error Back Propagation ◽  
The Neural Networks ◽  
Simulation Results

The chromatic aberration estimations of touch panel (TP) film by using neural networks are presented in this paper. The neural networks with error back-propagation (BP) learning algorithm were used to catch the complex relationship between the chromatic aberration, i.e., L.A.B. values, and the relative parameters of TP decoration film. An artificial intelligent (AI) estimator based on neural model for the estimation of physical property of TP film is expected to be developed. From the simulation results shown, the estimations of chromatic aberration of TP film are very accurate. In other words, such an AI estimator is quite promising and potential in commercial using.

Parity is not a generalisation problem

1997 ◽  
Vol 20 (1) ◽  
pp. 69-70
Author(s):  
R. I. Damper
Keyword(s):  
Empirical Analysis ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Mechanisms ◽  
Relevant Issue ◽  
Pure Type ◽  
Error Back Propagation ◽  
Parity Problem

Uninformed learning mechanisms will not discover “type- 2” regularities in their inputs, except fortuitously. Clark & Thornton argue that error back-propagation only learns the classical parity problem – which is “always pure type-2” – because of restrictive assumptions implicit in the learning algorithm and network employed. Empirical analysis showing that back-propagation fails to generalise on the parity problem is cited to support their position. The reason for failure, however, is that generalisation is simply not a relevant issue. Nothing can be gleaned about back-propagation in particular, or learning in general, from this failure.

scholarly journals Artificial neural networks employment in the prediction of evapotranspiration of greenhouse-grown sweet pepper

2016 ◽  
Vol 20 (6) ◽  
pp. 507-512 ◽  
Author(s):  
Héliton Pandorfi ◽  
Alan C. Bezerra ◽  
Roberto T. Atarassi ◽  
Frederico M. C. Vieira ◽  
José A. D. Barbosa Filho ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Meteorological Data ◽  
Back Propagation ◽  
Sweet Pepper ◽  
Coefficient Of Determination ◽  
Error Back Propagation ◽  
Artificial Neural

ABSTRACT This study aimed to investigate the applicability of artificial neural networks (ANNs) in the prediction of evapotranspiration of sweet pepper cultivated in a greenhouse. The used data encompass the second crop cycle, from September 2013 to February 2014, constituting 135 days of daily meteorological data, referring to the following variables: temperature and relative air humidity, wind speed and solar radiation (input variables), as well as evapotranspiration (output variable), determined using data obtained by load-cell weighing lysimeter. The recorded data were divided into three sets for training, testing and validation. The ANN learning model recognized the evapotranspiration patterns with acceptable accuracy, with mean square error of 0.005, in comparison to the data recorded in the lysimeter, with coefficient of determination of 0.87, demonstrating the best approximation for the 4-21-1 network architecture, with multilayers, error back-propagation learning algorithm and learning rate of 0.01.

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