Study of the Magnetic Properties of Haematite Based on Spectroscopy and the IPSO-ELM Neural Network

Mao, Yachun; Liu, Chongmin; Xiao, Dong; Wang, Jichun; Le, Ba Tuan

doi:10.1155/2018/6357905

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…The IPSO-ELM model aims to globally optimize the connection weights and hidden layer by relying on the advantages of the improved particle group procedure with strong worldwide exploration skill and fast junction speed [ 26 ] and then optimize the performance of the IPSO-ELM model. Prediction accuracy and prediction speed [ 27 ]. The specific operation steps are as follows, and the diagram of the IPSO-ELM model is revealed in Figure 1 [ 28 ].…”

Section: Models and Methodsmentioning

confidence: 99%

Research on Kinetic Energy Recovery of Energy Vehicle ABS Solenoid Valve Based on the ELM Deep Learning Model

Zhang

2022

Computational Intelligence and Neuroscience

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Aiming at the energy vehicle ABS kinetic energy recovery, this study optimizes the ABS system through the IPSO-ELM model, so that the energy vehicle can recover the energy generated by the ABS system to the greatest extent, so as to achieve the purpose of kinetic energy recovery and reduce energy consumption and vehicle cost. Based on the PSO-ELM model, a linear decreasing weighting method is introduced, and then, an IPSO-ELM model is proposed for the optimization analysis of ABS brake kinetic energy recovery. The results show that compared with the simple ELM model and PSO-ELM model, the simulation mean square error and relative error are significantly smaller, the generalization ability and prediction accuracy are higher, and the maximum relative error of the prediction result is 5.43% and the average relative error is 2.72%. The results confirm that the use of IPSO-ELM for ABS kinetic energy recovery optimization is extremely effective, and the study of ABS kinetic energy recovery for energy vehicles based on IPSO-ELM model optimization has strong application prospects and application potential.

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Section: Models and Methodsmentioning

confidence: 99%

Research on Kinetic Energy Recovery of Energy Vehicle ABS Solenoid Valve Based on the ELM Deep Learning Model

Zhang

2022

Computational Intelligence and Neuroscience

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“…This approach led to significant improvements in energy efficiency and sintering quality through the analysis of high-priority factors. Yachun Mao et al [ 33 ] introduced a detection method for the magnetic properties of limonite using the improved Particle Swarm Optimization–Enhanced Extreme Learning Machine (IPSO–ELM) algorithm and spectroscopy. The IPSO–ELM predictive model exhibited excellent performance and generalization capability compared to ELM and PSO–ELM predictive models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Biomass–Humic Acid Limonite Pellets Using Artificial Neural Network Model

et al. 2023

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Due to the detrimental impact of steel industry emissions on the environment, countries worldwide prioritize green development. Replacing sintered iron ore with pellets holds promise for emission reduction and environmental protection. As high-grade iron ore resources decline, research on limonite pellet technology becomes crucial. However, pellets undergo rigorous mechanical actions during production and use. This study prepared a series of limonite pellet samples with varying ratios and measured their compressive strength. The influence of humic acid on the compressive strength of green and indurated pellets was explored. The results indicate that humic acid enhances the strength of green pellets but reduces that of indurated limonite pellets, which exhibit lower compressive strength compared to bentonite-based pellets. Furthermore, artificial neural networks (ANN) predicted the compressive strength of humic acid and bentonite-based pellets, establishing the relationship between input variables (binder content, pellet diameter, and weight) and output response (compressive strength). Integrating pellet technology and machine learning drives limonite pellet advancement, contributing to emission reduction and environmental preservation.

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“…e PSO algorithm is a global search algorithm [30,31]. As a result, the PSO algorithm has a good adaptability and simplicity in practical applications [32][33][34][35]. We adopt the PSO algorithm to update the Lagrange multiplier in this paper.…”

Section: Introductionmentioning

confidence: 99%

An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

Zhou

et al. 2020

Shock and Vibration

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The fault rate in equipment increases significantly along with the service life of the equipment, especially for multiple fault. Typically, the Bayesian theory is used to construct the model of faults, and intelligent algorithm is used to solve the model. Lagrangian relaxation algorithm can be adopted to solve multiple fault diagnosis models. But the mathematical derivation process may be complex, while the updating method for Lagrangian multiplier is limited and it may fall into a local optimal solution. The particle swarm optimization (PSO) algorithm is a global search algorithm. In this paper, an improved Lagrange-particle swarm optimization algorithm is proposed. The updating of the Lagrangian multipliers is with the PSO algorithm for global searching. The difference between the upper and lower bounds is proposed to construct the fitness function of PSO. The multiple fault diagnosis model can be solved by the improved Lagrange-particle swarm optimization algorithm. Experiment on a case study of sensor data-based multiple fault diagnosis verifies the effectiveness and robustness of the proposed method.

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Study of the Magnetic Properties of Haematite Based on Spectroscopy and the IPSO-ELM Neural Network

Cited by 7 publications

References 20 publications

Research on Kinetic Energy Recovery of Energy Vehicle ABS Solenoid Valve Based on the ELM Deep Learning Model

Research on Kinetic Energy Recovery of Energy Vehicle ABS Solenoid Valve Based on the ELM Deep Learning Model

Prediction of Compressive Strength of Biomass–Humic Acid Limonite Pellets Using Artificial Neural Network Model

An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

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