ELM-NET, a closer to practice approach for classifying the big data using multiple independent ELMs

Shokrzade, Amin; Tab, Fardin Akhlaghian; Ramezani, Mohsen

doi:10.1007/s10586-019-02957-7

Cited by 3 publications

(1 citation statement)

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“…Based on the above problems, we propose a new robust training algorithm, which adds a memory with linear nodes and input tap delay lines for signal preprocessing. Since the output of each linear hidden node in SLFN is the sum of weighted input data, each node can be regarded as an FIR filter [20,21]. e hidden layer is designed as a set of lowpass filters, high-pass filters, band-pass filters, band-stop filters, or other filter types which are used to process input data with interference and undesired frequency components.…”

Section: Accuracy Detection Of Latent Fault Diagnosismentioning

confidence: 99%

[Retracted] Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network

Chen

Zhang

2021

Computational Intelligence and Neuroscience

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Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.

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Section: Accuracy Detection Of Latent Fault Diagnosismentioning

confidence: 99%