System identification using neuro fuzzy approach for IoT application
The Internet of Things (IoT) has become a popular application in recent years. However, it is the wireless communication mode. In such a scenario, the user would have to send information either nonlinear or dynamic data type in the form of a signal or an image, videos depending on the application. T...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917422001192 |
| _version_ | 1797997380186931200 |
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| author | Rakesh Kumar Pattanaik Srikanta Kumar Mohapatra Mihir Narayan Mohanty Binod Kumar Pattanayak |
| author_facet | Rakesh Kumar Pattanaik Srikanta Kumar Mohapatra Mihir Narayan Mohanty Binod Kumar Pattanayak |
| author_sort | Rakesh Kumar Pattanaik |
| collection | DOAJ |
| description | The Internet of Things (IoT) has become a popular application in recent years. However, it is the wireless communication mode. In such a scenario, the user would have to send information either nonlinear or dynamic data type in the form of a signal or an image, videos depending on the application. The proposed work is focused on this model identification that tends to nonlinear dynamic system identification for IoT applications. An Autoregressive Moving Average (ARMA) model represents model for IoT application. To verify the model supremacy, an ARMA bench mark system is used. The adaptiveness is proved through variation of weights and can be universally used for the next generation. In the first attempt, the Multilayer Perceptron model (MLP) is considered as the ARMA system and observed. Further, to improve its accuracy, the Adaptive Neuro-Fuzzy system (ANFIS) model is designed for system identification. It is shown in the result section that it identifies better than the MLP as well as traditional system identification techniques. |
| first_indexed | 2024-04-11T10:32:02Z |
| format | Article |
| id | doaj.art-01bbf473d7a94616a9f66473579dfb9a |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-11T10:32:02Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-01bbf473d7a94616a9f66473579dfb9a2022-12-22T04:29:24ZengElsevierMeasurement: Sensors2665-91742022-12-0124100485System identification using neuro fuzzy approach for IoT applicationRakesh Kumar Pattanaik0Srikanta Kumar Mohapatra1Mihir Narayan Mohanty2Binod Kumar Pattanayak3ITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, IndiaChitkara University Institute of Engineering & Technology, Chitkara University, Punjab, IndiaITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, IndiaITER, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India; Corresponding author.The Internet of Things (IoT) has become a popular application in recent years. However, it is the wireless communication mode. In such a scenario, the user would have to send information either nonlinear or dynamic data type in the form of a signal or an image, videos depending on the application. The proposed work is focused on this model identification that tends to nonlinear dynamic system identification for IoT applications. An Autoregressive Moving Average (ARMA) model represents model for IoT application. To verify the model supremacy, an ARMA bench mark system is used. The adaptiveness is proved through variation of weights and can be universally used for the next generation. In the first attempt, the Multilayer Perceptron model (MLP) is considered as the ARMA system and observed. Further, to improve its accuracy, the Adaptive Neuro-Fuzzy system (ANFIS) model is designed for system identification. It is shown in the result section that it identifies better than the MLP as well as traditional system identification techniques.http://www.sciencedirect.com/science/article/pii/S2665917422001192Adaptive neuro fuzzy systemWireless sensor Network channelNonlinear dynamic system IdentificationInternet of thingsAutoregressive moving average |
| spellingShingle | Rakesh Kumar Pattanaik Srikanta Kumar Mohapatra Mihir Narayan Mohanty Binod Kumar Pattanayak System identification using neuro fuzzy approach for IoT application Measurement: Sensors Adaptive neuro fuzzy system Wireless sensor Network channel Nonlinear dynamic system Identification Internet of things Autoregressive moving average |
| title | System identification using neuro fuzzy approach for IoT application |
| title_full | System identification using neuro fuzzy approach for IoT application |
| title_fullStr | System identification using neuro fuzzy approach for IoT application |
| title_full_unstemmed | System identification using neuro fuzzy approach for IoT application |
| title_short | System identification using neuro fuzzy approach for IoT application |
| title_sort | system identification using neuro fuzzy approach for iot application |
| topic | Adaptive neuro fuzzy system Wireless sensor Network channel Nonlinear dynamic system Identification Internet of things Autoregressive moving average |
| url | http://www.sciencedirect.com/science/article/pii/S2665917422001192 |
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