Information fusion method of multichannel nanosensors based on neural network

• Autorzy: Li Chaoke

Identyfikatory

DOI

10.24425/bpasts.2022.140258

• Języki publikacji: EN

Abstrakty

EN

Information fusion approaches have been commonly used in multi sensor environments for the fusion and grouping of data from various sensors which is used further to draw a meaningful interpretation of the data. Traditional information fusion methods have limitations such as high time complexity of fusion processes and poor recall rate. In this work, a new multi-channel nano sensor information fusion method based on a neural network has been designed. By analyzing the principles of information fusion methods, the back propagation based neural network (BP-NN) is devised in this work. Based on the design of the relevant algorithm flow, information is collected, processed, and normalized. Then the algorithm is trained, and output is generated to achieve the fusion of information based on multi-channel nano sensor. Moreover, an error function is utilized to reduce the fusion error. The results of the present study show that compared with the conventional methods, the proposed method has quicker fusion (integration of relevant data) and has a higher recall rate. The results indicate that this method has higher efficiency and reliability. The proposed method can be applied in many applications to integrate the data for further analysis and interpretations.

Słowa kluczowe

EN

nanosensors; multiplexing; information fusion; data fusion; neural network

PL

nanoczujniki; multipleksowanie; fuzja informacji; fuzja danych; sieć neuronowa

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2022
• Tom: Vol. 70, nr 2
• Strony: art. no. e140258
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Li Chaoke

• School of Intelligent Medical Engineering, Sanquan College of Xinxiang Medical University, Xinxiang 453003, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).