OS-ELM Based Real-Time RFID Indoor Positioning System for Shop-Floor Management

Yang, Zhi-Xin; Chen, Lei

doi:10.1007/978-3-319-14066-7_23

Cited by 1 publication

(1 citation statement)

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“…However, in many practical applications, such as the real-time radio-frequency identification (RFID) indoor positioning system for shop-floor management (Yang et al, 2015) and time series prediction (Lu et al, 2017), data are acquired sequentially, and CELM has to retrain all of the CELM processes to account for the new data. This causes a large amount of unnecessary training time.…”

Section: Discussionmentioning

confidence: 99%

Sin Activation Structural Tolerance of Online Sequential Circular Extreme Learning Machine

Atsawaraungsuk¹,

Katanyukul²

2017

IJTech

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This article discusses the development of the online sequential circular extreme learning machine (OS-CELM) and structural tolerance OS-CELM (STOS-CELM). OS-CELM is developed based on the circular extreme learning machine (CELM) to enable sequential learning. It can update a new chunk of data by spending less training time to update the chunk than the batch CELM. STOS-CELM is developed based on an idea similar to that of OS-CELM, but with a Householder block exact inverse QR decomposition (QRD) recursive least squares (QRD-RLS) algorithm to allow sequential learning and mitigate the criticality of deciding the number of hidden nodes. In addition, our experiments have shown that given the same hidden node setting, STOS-CELM can deliver accuracy comparable to a batch CELM approach and also has higher accuracy than the original online sequential extreme learning machine (OS-ELM) and structural tolerance OS-ELM (STOS-ELM) in classification problems, especially those involving high dimension datasets.

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Section: Discussionmentioning

confidence: 99%