Performance Analysis of Cellular Radio System Using Artificial Neural Networks

Gupta, Kriti Priya; Jain, Madhu

doi:10.11648/j.ajnna.20170301.12

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Back-propagation algorithm is used to train the network. The detailed methodology of the algorithm of the backpropagation algorithm is described in literature [8] [9]. In the representative figure shown in Figure-1, three layers are shown.…”

Section: Development Of Artificial Neural Network Modelmentioning

confidence: 99%

Application of Artificial Neural Network for Flow Stress Modelling of Steel

Rath¹

2017

AJNNA

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Abstract:The hot rolling mills of steel plants are in the process of transformation from manual operation to artificial intelligence (AI) based automatic operations. Most of the mill input parameters required by the automation system are recorded from different sensors installed in the mill except the flow stress of rolled material. Generally a semi-empirical equation is used that correlate flow stress with strain, strain rate and temperature during rolling. The coefficients and exponents of the empirical equations are calculated from experimental data with parameter estimation techniques. This paper discusses the application of artificial neural network (ANN) for calculation of flow stress of material from experimental data. Experiments were conducted in a dynamic thermo-mechanical simulator to measure flow stress of steel at different strain, strain rate and temperature. The experimental data was used to calculate coefficients of empirical equations using multivariable optimization techniques. The data was also used to formulate an ANN model using feed forward network. The ANN model was trained with backpropagation algorithm. The ANN method is found to be more accurate than the semi-empirical equations for correlating the flow stress with strain, strain rate and temperature.

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Section: Development Of Artificial Neural Network Modelmentioning

confidence: 99%

Application of Artificial Neural Network for Flow Stress Modelling of Steel

Rath¹

2017

AJNNA

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“…Modern intellectual means of analysis and prediction allow, with sufficiently high accuracy, regardless of the subject area, to determine the development trend of the researched variables based on the processing of large arrays of source data and detecting implicit, hidden patterns [1,2]. There are works on neural network prediction of stock prices and currency rates [3,4], credit scoring tasks, business strategies and logistics of e-commerce centers [5,6,7], features of radio signal propagation [8], pattern, speech and emotion recognition [9,10,11,12], research in medicine [13,14]. Separate articles, describing the attempts to predict students' progress, were published [15].…”

Section: Introductionmentioning

confidence: 99%

Intellectual prediction of student performance: opportunities and results

Zolotaryuk¹,

Zavgorodniy²,

Gorodetskaya³

2019

Proceedings of the 1st International Scientific Conference "Modern Management Trends and the Digital Economy: From Regional Dev

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Intellectual tools for analysis and forecasting are widely used in various fields-economics, medicine, technology, and linguistics. This article examines the possibilities of neural network forecasting of student performance. A general statement of the research object is formulated. Comprehensively considered and classified factors affecting student performance are preuniversity, university and psychophysiological ones. The features of the collection of information in Russian universities for intellectual analysis and forecasting are considered. Using the example of the database of the Financial University under the Government of the Russian Federation and additional information obtained by survey about factors, not present in the database, significant factors were determined using the correlation analysis toolset of the IBM SPSS Statistics statistical analysis package, which made it possible to reduce their number almost fourfold and record the regression model in a simpler form. Further research was carried out using a Deductor Studio analytical platform for intellectual processing and knowledge extraction. A multilayer neural network with nine entrance signs and one or two effective ones was built and trained. The effective entrance signs were taken as the results of the first year students taking senior exams. The research results showed that the predicted values of progress do not differ significantly from the actual ones. Consequently, neural network machine study technologies provide intelligent prediction of progress based on an analysis of the preceding factor signsof both the first and subsequent years. The directions of further research with the use of modern means of machine study are outlined.

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Performance Analysis of Cellular Radio System Using Artificial Neural Networks

Cited by 2 publications

References 11 publications

Application of Artificial Neural Network for Flow Stress Modelling of Steel

Application of Artificial Neural Network for Flow Stress Modelling of Steel

Intellectual prediction of student performance: opportunities and results

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