scholarly journals Feature subset selection in dynamic stability assessment power system using artificial neural networks

2015 ◽  
Vol 18 (2) ◽  
pp. 15-24
Author(s):  
Au Ngoc Nguyen ◽  
Anh Huy Nguyen ◽  
Binh Thi Thanh Phan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power System ◽  
Dynamic Stability ◽  
Subset Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Stability Assessment ◽  
Classification Rate ◽  
Artificial Neural

This paper presents method of feature subset selection in dynamic stability assessment (DSA) power system using artificial neural networks (ANN). In the application of ANN on DSA power system, feature subset selection aims to reduce the number of training features, cost and memory computer. However, the major challenge is to reduce the number of features but classification rate gets a high accuracy. This paper proposes applying Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS) and Feature Ranking (FR) algorithm to feature subset selection. The effectiveness of the algorithms was tested on the GSO-37bus power system. With the same number of features, the calculation results show that SFS algorithm yielded higher classification rate than FR, SBS algorithm. SFS algorithm yielded the same classification rate as SFFS algorithm.

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Structural Features for Furan-Derived Fruity and Meaty Aroma Impressions

2016 ◽  
Vol 11 (10) ◽  
pp. 1934578X1601101
Author(s):  
Bettina Wailzer ◽  
Johanna Klocker ◽  
Peter Wolschann ◽  
Gerhard Buchbauer
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Structural Features ◽  
Classification Rule ◽  
Classification Rate ◽  
Furan Derivatives ◽  
Information Index ◽  
The Neural Network ◽  
Artificial Neural

Furan derivatives are part of nearly all food aromas. They are mainly formed by thermal degradation of carbohydrates and ascorbic acid and from sugar-amino acid interactions during food processing. Caramel-like, sweet, fruity, nutty, meaty, and burnt odor impressions are associated with this class of compounds. In the presented work, structure-activity relationship (SAR) investigations are performed on a series of furan derivatives in order to find structural subunits, which are responsible for the particular characteristic flavors. Therefore, artificial neural networks are applied on a set of 35 furans with the aroma categories “meaty” or “fruity” to calculate a classification rule and class boundaries for these two aroma impressions. By training a multilayer perceptron network architecture with a backpropagation algorithm, a correct classification rate of 100% is obtained. The neural network is able to distinguish between the two studied groups by using the following significant descriptors as inputs: number of sulfur atoms, Looping Centric Information Index, Folding Degree Index and Petitjean Shape Indices. Finally, the results clearly demonstrate that artificial neural networks are successful tools to investigate non-linear qualitative structure-odor relationships of aroma compounds.

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