Prediction of Parameters of Semiconductor Band-pass Filters using Artificial Neural Network

Rusen, Vaiva; Krukonis, Audrius; Plonis, Darius

doi:10.1109/aieee51419.2021.9435748

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…In data prediction, the commonly used methods include linear regression [14], decision trees [15], etc., and the backpropagation neural network (BPNN) algorithm is also one of them [16]. The BPNN algorithm has good performance in processing nonlinear data [17] and is flexible and adaptable, making it widely used in finance and health care for data prediction [18].…”

Section: Back-propagation Neural Network Algorithmmentioning

confidence: 99%

The Prediction of Douyin Live Sales based on Neural Network Algorithms

Sun,

De,

Zheng

2023

HighTech. Innov. J.

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This paper aims to optimize neural network algorithms in order to improve their predictive performance and meet the demand for Douyin live sales forecasting. The Douyin live data of Florasis between January 2022 and June 2022 were collected from Huitun data and preprocessed. Using the Pearson correlation coefficient, six factors that are highly correlated with live sales were selected for subsequent prediction. This paper briefly introduces the back-propagation neural network (BPNN) algorithm and analyzes its parameter optimization methods, including particle swarm optimization (PSO), the artificial bee colony (ABC) algorithm, and the beetle antler search (BAS) algorithm. Then, an improved beetle antennae search (IBAS) algorithm was developed by introducing inertia weight and used to construct an IBAS-BPNN model for predicting sales volume in Douyin live streaming. The results showed that compared with the BPNN, PSO-BPNN, and ABC-BPNN algorithms, the IBAS-BPNN algorithm had better prediction performance, with a root-mean-square error of 335.6694, a mean absolute percentage error of 0.0532%, an equilibrium coefficient of 0.9889, and a shorter training time of 90.07 s. The experimental results demonstrate the reliability of the IBAS-BPNN algorithm for predicting sales volume in Douyin live streaming, providing new insights into parameter optimization of BPNN and offering references for further research on BPNN parameter optimization. It also provides an effective method with both timeliness and high accuracy for predicting sales volume in Douyin live streaming in practical applications. Doi: 10.28991/HIJ-2023-04-02-09 Full Text: PDF

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Section: Back-propagation Neural Network Algorithmmentioning

confidence: 99%

The Prediction of Douyin Live Sales based on Neural Network Algorithms

Sun,

De,

Zheng

2023

HighTech. Innov. J.

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“…The prediction is usually very fast. Therefore, the procedure of analysis or synthesis of microwave devices is performed significantly faster [33,34]. The prediction could be performed thousands of times faster than calculations by the conventional methods [35].…”

Section: Introductionmentioning

confidence: 99%

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

et al. 2022

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The usage of techniques of the artificial neural networks (ANNs) in the field of microwave devices has recently increased. The advantages of ANNs in comparison with traditional full-wave methods are that the prediction speed when the traditional time-consuming iterative calculations are not required and also the complex mathematical model of the microwave device is no longer needed. Therefore, the design of microwave device could be repeated many times in real time. However, methods of artificial neural networks still lag behind traditional full-wave methods in terms of accuracy. The prediction accuracy depends on the structure of the selected neural network and also on the obtained dataset for the training of the network. Therefore, the paper presents a systematic review of the implementation of ANNs in the field of the design and analysis of microwave devices. The guidelines for the systematic literature review and the systematic mapping research procedure, as well as the Preferred Report Items for Systematic Reviews and Meta-Analysis statements (PRISMA) are used to conduct literature search and report the results. The goal of the paper is to summarize the application areas of usage of ANNs in the field of microwave devices, the type and structure of the used artificial neural networks, the type and size of the dataset, the interpolation and the augmentation of the training dataset, the training algorithm and training errors and also to discuss the future perspectives of the usage of ANNs in the field of microwave devices.

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“…Disertacijos tema yra atspausdinti 6 moksliniai straipsniai: 2recenzuojamuose mokslo žurnaluose, įtrauktuose į Clarivate Analytics Web of Science sąrašą (Plonis et al, 2019;Rusen et al, 2021a); 2recenzuojamoje konferencijų medžiagoje, įtrauktoje į Clarivate Analytics Web of Science sąrašą (Šlegerytė et al, 2019;Rusen et al, 2021b), 2recenzuojamoje IEEE Xplore tarptautinių konferencijos medžiagoje (Rusen et al, 2021c;Rusen ir Plonis, 2021). Disertacijoje atliktų tyrimų rezultatai buvo paskelbti aštuoniose mokslinėse konferencijose Lietuvoje ir užsienyje:…”

Section: Darbo Rezultatų Aprobavimasunclassified

“…Skyriaus tema paskelbti du autorės straipsniai (Plonis et al, 2019;Rusen et al, 2021c), skyriaus medžiaga pristatyta vienoje mokslinėje konferencijoje.…”

Section: Valdomų Parametrų Daugiasluoksnių Mikrobangų įTaisų Charakte...unclassified

“…The research results were published in six scientific publications: two publications in scientific journals referenced in the Clarivate Analytics Web of Science database with the citation index (Plonis et al, 2019;Rusen et al, 2021a); two publications in conference proceedings referenced in the Clarivate Analytics Web of Science database (Šlegerytė et al, 2019;Rusen et al, 2021b); and two publications in conference proceedings referenced in the IEEE Xplore database (Rusen et al, 2021c;Rusen and Plonis, 2021)…”

Section: Approval Of the Research Findingsmentioning

confidence: 99%

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Daugiasluoksnių puslaidininkinių ir dielektrinių mikrobangų įtaisų tyrimas

Rusen¹

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Vadovas prof. dr. Darius PLONIS (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija -T 001). Vilniaus Gedimino technikos universiteto elektros ir elektronikos mokslo krypties disertacijos gynimo taryba: Pirmininkas prof. dr. Algirdas BAŠKYS (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija -T 001). Nariai: doc. dr. Darius ANDRIUKAITIS (Kauno technologijos universitetas, elektros ir elektronikos inžinerija -T 001), prof. habil. dr. Yevhen YASHCHYSHYN (Varšuvos technologijų universitetas, Lenkija, elektros ir elektronikos inžinerija -T 001), prof. dr. Dalius MATUZEVIČIUS (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija -T 001), prof. dr. Vitalij NOVICKIJ (Vilniaus Gedimino technikos universitetas, elektros ir elektronikos inžinerija -T 001). Disertacija bus ginama viešame elektros ir elektronikos inžinerijos mokslo krypties disertacijos gynimo tarybos posėdyje 2023 m. gegužės 30 d. 10 val. Vilniaus Gedimino technikos universiteto senato posėdžių salėje.

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Prediction of Parameters of Semiconductor Band-pass Filters using Artificial Neural Network

Cited by 5 publications

References 10 publications

The Prediction of Douyin Live Sales based on Neural Network Algorithms

The Prediction of Douyin Live Sales based on Neural Network Algorithms

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

Daugiasluoksnių puslaidininkinių ir dielektrinių mikrobangų įtaisų tyrimas

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