Optimum block-adaptive learning algorithm for error back-propagation networks

1992 ◽  
Vol 40 (12) ◽  
pp. 3032-3042 ◽  
Author(s):  
Li-Min Du ◽  
Zi-Qiang Hou ◽  
Qi-Hu Li
Keyword(s):  
Adaptive Learning ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Error Back Propagation

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.

Gravity Compensation for Manipulator Control by Neural Networks with Partially Preorganized Structure

1990 ◽  
Vol 2 (4) ◽  
pp. 282-287
Author(s):  
Toshio Tsuji ◽  
◽  
Masataka Nishida ◽  
Toshiaki Takahashi ◽  
Koji Ito
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Center Of Mass ◽  
Back Propagation ◽  
Equation Of Motion ◽  
Point Load ◽  
Gravity Compensation ◽  
Error Back Propagation ◽  
Industrial Manipulators ◽  
Parameter Values

The gravity torque of a manipulator can be compensated if the equation of motion can be correctly introduced, but in general industrial manipulators, there are many cases when the parameter values such as the position of center of mass are not clear, and these values largely change by the exchange of hand portions and the grasping of substances. Furthermore, in addition to unclear parameters, there are factors which occur by structural gravity compensation (spring and counter-balance) and which in many cases are difficult to express with the equation of motion. In this paper, compensation of the gravity torque of the manipulator is studied by, the use of neural networks. For this purpose, a model which makes the structure known to be contained in mapping as a unit with preorganized characteristics prepared in parallel with hidden unit of error back propagation-type neural network is proposed, by which the characteristics of the link system which is the object for learning can be imbedded into the network as preorganized knowledge beforehand. Finally, the results of experiments done with the use of industrial manipulators are given, and it is made clear that the compensation of gravity torque of manipulator and adaptive learning for end-point load are possible by the use of this model.

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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