On the application of artificial neural network in analyzing and studying daily loads of Jordan power system plant

Najim, Salam A.; Al-Omari, Zakaria; Said, Samir M.

doi:10.2298/csis0801127n

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…Najim et al [18] describes how a multilayered perceptron back propagation approach can forecast electric power load curves based on the variety of parameters, as well as a neural network capable of forecasting short-term power load. Abuadlla et al in his work [1] deals with issues of attack on a large network.…”

Section: Application Of Multilayered Perceptrons From the Aspect Of Tmentioning

confidence: 99%

A system for deductive prediction and analysis of movement of basketball referees

Pecev

Racković

Ivković

2015

Multimed Tools Appl

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This paper presents us with an automatic prediction and analysis of basketball referees movement which is useful for educational software. Such software would be very beneficial in training the young basketball referees. The paper proposes that the movement prediction of basketball referees can be achieved with a multilayered perceptron neural network. Network will reason on the basis of a ball movement during a play action. Proposed neural network will be trained with a modified Back Propagation algorithm which essentially presents a special algorithm for a multiple dependent Time Series prediction. In this paper, we will also describe initial designs of a neural network structure that, we believe, would better suit the nature of a multiple dependent Time Series prediction problems. The aforementioned educational software is capable of determining whether a referee was moving properly in a certain situation or not. Determination is possible on the basis of numerical values that are calculated by simulating the human visual field. The referee's horizontal field of view simulation is based on the standard set by the American Optometric Association. It is implemented through a modified Sweep and Prune algorithm which is also discussed in this paper.Keywords Multiple dependent time series · MLP neural networks · LTR -MDTS model · Field of vision simulation · Basketball referee movement Predrag Pecev

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Section: Application Of Multilayered Perceptrons From the Aspect Of Tmentioning

confidence: 99%

A system for deductive prediction and analysis of movement of basketball referees

Pecev

Racković

Ivković

2015

Multimed Tools Appl

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“…Vintan et al in paper [3] use MLP (Multilayered Perceptron) back propagation neural networks to predict a person's movement within a building based on a Time Series. Najim et al in their work [18] present a multilayered perceptron back propagation approach to forecast electric power load curves based on various parameters, and presents a neural network for short-term load forecasting while Abuadlla et al in their work [19] deal with issues of attacks on large network and computer systems where MLP network was used to select time windows where attacks are more probable based on the significant changes in traffic over time.…”

Section: Related Workmentioning

confidence: 99%

LTR-MDTS structure - a structure for multiple dependent time series prediction

Pecev

Racković

2017

COMSIS J

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The subject of research presented in this paper is to model a neural network structure and appropriate training algorithm that is most suited for multiple dependent time series prediction / deduction. The basic idea is to take advantage of neural networks in solving the problem of prediction of synchronized basketball referees' movement during a basketball action. Presentation of time series stemming from the aforementioned problem, by using traditional Multilayered Perceptron neural networks (MLP), leads to a sort of paradox of backward time lapse effect that certain input and hidden layers nodes have on output nodes that correspond to previous moments in time. This paper describes conducted research and analysis of different methods of overcoming the presented problem. Presented paper is essentially split into two parts. First part gives insight on efforts that are put into training set configuration on standard Multi Layered Perceptron back propagation neural networks, in order to decrease backwards time lapse effects that certain input and hidden layers nodes have on output nodes. Second part of paper focuses on the results that a new neural network structure called LTR-MDTS provides. Foundation of LTR-MDTS design relies on a foundation on standard MLP neural networks with certain, left-to-right synapse removal to eliminate aforementioned backwards time lapse effect on the output nodes.

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Artificial Neural Network based Smart and Energy Efficient Street Lighting System: A Case Study for Residential area in Hosur

Mohandas

Dhanaraj

Gao

2019

Sustainable Cities and Society

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On the application of artificial neural network in analyzing and studying daily loads of Jordan power system plant

Cited by 8 publications

References 11 publications

A system for deductive prediction and analysis of movement of basketball referees

A system for deductive prediction and analysis of movement of basketball referees

LTR-MDTS structure - a structure for multiple dependent time series prediction

Artificial Neural Network based Smart and Energy Efficient Street Lighting System: A Case Study for Residential area in Hosur

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