On Training Sample Selection for Artificial Neural Networks using Number-Theoretic Methods

2009 ◽  
Author(s):  
F. Tong ◽  
X.L. Liu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sample Selection ◽  
Training Sample ◽  
Selection For ◽  
Artificial Neural ◽  
Training Sample Selection

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

ANALYSIS OF DOCUMENTS RANKING ALGORITHMS BASED ON THE ARTIFICIAL NEURAL NETWORKS

2019 ◽  
Author(s):  
Andrew Lishchytovych ◽  
Volodymyr Pavlenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Search Algorithm ◽  
Network Algorithms ◽  
Text Document ◽  
Ranking Algorithms ◽  
Selection For ◽  
Artificial Neural

The object of this study is to analyse the effectiveness of document ran­ king algorithms in search engines that use artificial neural networks to match the texts. The purpose of the study was to inspect a neural network model of text document ran­ king that uses clustering, factor analysis, and multi-layered network architecture. The work of neural network algorithms was compared with the standard statistical search algorithm OkapiBM25. The result of the study is to evaluate the effectiveness of the use of particular models and to recommend model selection for specific datasets.

Selection for Wandering Behavior in a Small Robot

1994 ◽  
Vol 2 (1) ◽  
pp. 101-116 ◽  
Author(s):  
Orazio Miglino ◽  
Kourosh Nafasi ◽  
Charles E. Taylor
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Complexity ◽  
Random Noise ◽  
Effective Means ◽  
Selection For ◽  
Artificial Neural ◽  
Small Robot ◽  
Fixed Period

We have evolved artificial neural networks to control the wandering behavior of small robots. The task and environment were very simple—to touch as many squares in a grid as possible during a fixed period of time. A number of the simulated robots were embodied in a small Lego™ robot, controlled by a Motorola™ 6811 processor; and their performance was compared to the simulations. We observed that: (a) evolution was an effective means to program the robot's behavior; (b) progress was characterized by sharply stepped periods of improvement, separated by periods of stasis that corresponded to levels of behavioral/computational complexity; and (c) the simulated and realized robots behaved quite similarly, the realized robots in some cases outperforming the simulated ones. Introducing random noise to the simulations improved the fit somewhat (from r = 0.73 to 0.79). Hybrid simulated/embodied selection regimes for evolutionary robots are discussed.

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