Comparing performance of Backpropagation and RBF neural network models for predicting daily network traffic

Purnawansyah,; Haviluddin, .

doi:10.1109/miceei.2014.7067332

Cited by 13 publications

(6 citation statements)

References 12 publications

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“…Where, the hidden layer as a place to update and adjust the weight. Thus, the new weight values are obtained then directed towards the desired output target [14] [15]. The BPNN architecture and flowchart can be seen in Figure 1 and…”

Section: Methodsmentioning

confidence: 99%

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Aini¹,

Haviluddin

2019

Kno. Eng. Da. Sc.

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Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019.

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Section: Methodsmentioning

confidence: 99%

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Aini¹,

Haviluddin

2019

Kno. Eng. Da. Sc.

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“…Metode JST merupakan metode yang sangat baik untuk mendapatkan hasil peramalan yang lebih baik (Aini et al, 2019;Haviluddin & Dengen, 2016;Ma et al, 2016;Majhi et al, 2014;Purnawansyah & Haviluddin, 2014;Sakinah et al, 2018;Simanungkalit et al, 2020;Syafiq et al, 2020). Model akurasi peramalan terbaik yang digunakan dalam JST adalah MSE Simanungkalit et al, 2020).…”

Section: Pendahuluanunclassified

Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)

Puspitasari

Haviluddin

Kustiawan

et al. 2021

JISKa

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The automotive industry in Indonesia, primarily cars, is getting more and more varied. Along with increasing the number of vehicles, Brand Holder Sole Agents (ATPM) compete to provide after-sale services (mobile service). However, the company has difficulty knowing the rate of growth in the number of mobile services handled, thus causing losses that impact sources of income. Therefore, we need a standard method in determining the forecasting of the number of car services in the following year. This study implements the Backpropagation Neural Network (BPNN) method in forecasting car service services (after-sale) and Mean Square Error (MSE) for the process of testing the accuracy of the forecasting results formed. The data used in this study is car service data (after-sale) for the last five years. The results show that the best architecture for forecasting after-sales services using BPNN is the 5-10-5-1 architectural model with a learning rate of 0.2 and the learning function of trainlm and MSE of 0.00045581. This proves that the BPNN method can predict mobile service (after-sale) services with good forecasting accuracy values.

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“…Reference [11] presents techniques based on the development of BP neural network and radial basis function (RBF) neural network models, for modeling and predicting the daily network traffic at Universities East Kalimantan, Indonesia. The predicting daily network traffic usage is a very important issue in the service activities of the university.…”

Section: Related Workmentioning

confidence: 99%

Design and analysis of a general data evaluation system based on social networks

Mao

et al. 2018

J Wireless Com Network

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A social network is a social structure with a set of social actors and other social interactions between actors. The social network supports a set of methods to explain the structure of whole social entities as well as a variety of theories in these structures. The study of these structures uses social network analysis to explain local and global patterns and examine network dynamics. The related economic data is important for our economic developments. We could analyze the economic data to explore the investments. In this paper, we focus on the design and analysis of automatic management systems to make a decision about the petroleum projects. Petroleum is the lifeblood of economic development. So, we need an automatic and effective management system to make the mining investments of petroleum. Before deciding to invest in the petroleum project, the engineers will describe this project by providing the sufficient economic data. Based on this data, the decision can undertake professional analysis, to determine whether the project is feasible. To automate the manual process and overcome the traditional evaluation method, the BP (back propagation) neural network is applied in a petroleum project economic evaluation. This method opens up a new way for the evaluation of petroleum projects. In this article, we first introduce the application of BP neural network. Then, we introduce the principle and shortcoming of BP neural network. Based on it, we optimize the BP neural network. Secondly, according to the characteristics of petroleum projects, we obtain the economic evaluation indicators about petroleum projects, which are used as the input of the BP neural network. Thirdly, we propose the design of the BP neural network and select the smallest simulation error of the BP neural network as a forecasting model of petroleum projects. In order to improve the accuracy, the labeled training samples are selected for BP neural network training. Finally, based on extensive simulation experimental results, we demonstrate our scheme about the BP neural network of petroleum project evaluation is effective and has overperformance than traditional schemes in terms of convenience and accurate decision-making suggestions.

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Comparing performance of Backpropagation and RBF neural network models for predicting daily network traffic

Cited by 13 publications

References 12 publications

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN)

Design and analysis of a general data evaluation system based on social networks

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