Selection of training data for neural networks by a genetic algorithm

1998 ◽  
pp. 633-642 ◽  
Author(s):  
Colin R. Reeves ◽  
Stewart J. Taylor
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Training Data ◽  
Selection Of

scholarly journals Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

2019 ◽  
Vol 66 (3) ◽  
pp. 363-388
Author(s):  
Serkan Aras ◽  
Manel Hamdi
Keyword(s):  
Neural Networks ◽  
Crude Oil ◽  
Significant Interaction ◽  
Statistical Tests ◽  
Oil Price ◽  
Training Data ◽  
Forecasting Model ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Selection Of

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.

Information Based Selection of Neural Networks Training Data for S.I. Engine Mapping

2001 ◽  
Author(s):  
Ivan Arsie ◽  
Fabrizio Marotta ◽  
Cesare Pianese ◽  
Gianfranco Rizzo
Keyword(s):  
Neural Networks ◽  
Training Data ◽  
Selection Of

Results of Bias-variance Tests on Multi-layer Perceptron Neural Networks

2001 ◽  
Vol 5 (5) ◽  
pp. 300-305
Author(s):  
Wimpie D. Nortje ◽  
◽  
Johann E. W. Holm ◽  
Gerhard P. Hancke ◽  
Imre. J. Rudas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Bayesian Networks ◽  
Training Data ◽  
Computational Time ◽  
Bayesian Techniques ◽  
Multiple Networks ◽  
The Neural Networks ◽  
Computational Errors ◽  
Bias Variance ◽  
Selection Of

Training neural networks involves selection of a set of network parameters, or weights, on account of fitting a non-linear model to data. Due to the bias in the training data and small computational errors, the neural networks’ opinions are biased. Some improvement is possible when multiple networks are used to do the classification. This approach is similar to taking the average of a number of biased opinions in order to remove some of the bias that resulted from training. Bayesian networks are effective in removing some of the bias associated with training, but Bayesian techniques are tedious in terms of computational time. It is for this reason that alternatives to Bayesian networks are investigated.

Soil Moisture Retrieval Based on ASAR Data and Genetic Neural Networks

2012 ◽  
Vol 500 ◽  
pp. 198-203
Author(s):  
Chang Lin Xiao ◽  
Yan Chen ◽  
Lina Liu ◽  
Ling Tong ◽  
Ming Quan Jia
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Soil Moisture ◽  
Training Data ◽  
Neural Network Training ◽  
Practical Test ◽  
Network Training

Genetic Algorithm can further optimize Neural Networks, and this optimized Algorithm has been used in many fields and made better results, but currently, it have not been used in inversion parameters. This paper used backscattering coefficients from ASAR, AIEM model to calculate data as neural network training data and through Genetic Algorithm Neural Networks to retrieve soil moisture. Finally compared with practical test and shows the validity and superiority of the Genetic Algorithm Neural Networks.

scholarly journals Evolutionary Artificial Neural Networks as Tools for Predicting the Internal Structure of Microemulsions

2008 ◽  
Vol 11 (1) ◽  
pp. 67 ◽  
Author(s):  
M. Gašperlin ◽  
F. Podlogar ◽  
R. Šibanc
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Drug Delivery Systems ◽  
Drug Carrier ◽  
Computer Language ◽  
Development Cost ◽  
Scanning Calorimetry ◽  
Evolutionary Neural Networks ◽  
Further Development ◽  
Selection Of

PURPOSE. The purpose of this study was to predict microemulsion structures by creating two artificial evolutionary neural networks (ANN) combined with a genetic algorithm. The first ANN would be able to determine the type of microemulsion from the desired composition, and the second to determine the type of microemulsion directly from a differential scanning calorimetry (DSC) curve. METHODS. The algorithms and the structures for each ANN were constructed and programmed in C++ computer language. The ANNs had a feed forward structure with one hidden level and were trained using a genetic algorithm. DSC was used to determine the microemulsion type. RESULTS. The ANNs showed very encouraging accuracy in predicting the microemulsion type from its composition and also directly from the DSC curve. The percentage success, calculated over the tested data, was over 90%. This enabled us, with satisfactory accuracy, to construct several pseudoternary diagrams that could facilitate the selection of the microemulsion composition to obtain the optimal desired drug carrier. CONCLUSIONS. The ANN constructed here, enhanced with a genetic algorithm, is an effective tool for predicting the type of microemulsion. These findings provide the basis for reducing research time and development cost for characterizing microemulsion properties. Its application would stimulate the further development of such colloidal drug delivery systems, exploit their advantages and, to a certain extent, avoid their disadvantages.

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