Quantitative analysis of sinters using laser-induced breakdown spectroscopy (LIBS) coupled with kernel-based extreme learning machine (K-ELM)

Abstract

This work explores the combination of LIBS technology and K-ELM algorithm for the quantitative analysis of total iron (TFe) content and alkalinity of sinter.

Rapid Determination of Wood and Rice Husk Pellets’ Proximate Analysis and Heating Value

“…Extreme learning machine (ELM) is a recently developed machine learning algorithm based on single-hidden layer feedforward neural networks (SLFNN), and it runs in a simpler and easier way than traditional neural network methods [42,43]. Output weights can be obtained by randomly initializing input weights and hidden layer biases in global optimization [44,45]. In particular the number of hidden layer biases is obtained by continuous optimization in a predefined range which is usually less than the number of samples for modeling.…”

Correction of moisture interference in laser-induced breakdown spectroscopy detection of coal by combining neural networks and random spectral attenuation

“…1 In the pricing and blending of coal, the content of coal is a critical basis. 2,3 The volatile matter in coal is used as a design parameter of boilers. 4 It is necessary to realize the online analysis of coal to improve economic efficiency in power industries.…”

Composition analysis of ceramic raw materials using laser-induced breakdown spectroscopy and autoencoder neural network

“…20 The chemical element composition and content of ceramic raw materials are quite different, which leads to serious matrix effects that will reduce the accuracy of the quantitative analysis model. 21 Multivariate analysis methods, such as SVM, 22,23 PLS, [24][25][26] and limit learning machine 31 have been widely used in LIBS qualitative and quantitative analysis. In recent years, inspired by the success of articial neural networks (ANN) in the eld of articial intelligence, 32,33 more and more researchers tried to apply ANN to quantitative analysis including Back Propagation Neural Network (BPNN) [27][28][29] and convolutional neural network (CNN).…”