On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network

Xie, Yongfang; Yu, Jinjing; Xie, Shiwen; Huang, Tingwen; Gui, Weihua

doi:10.1016/j.neunet.2019.03.007

Cited by 29 publications

(11 citation statements)

References 41 publications

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“…From the implementation point of view, the initialization stage is considered as crucial from the point of view of the accuracy and stability of the RBF-based approximations [58]. Thus, to design highly accurate and effective RBFN, it is important to apply an effective strategy for deciding on center locations and prototype selection.…”

Section: B Rbf Network Designmentioning

confidence: 99%

“…To implement the idea, an algorithm which, could automatically select the RBFN configuration and integrate it with the training process into a single task would be required. Such an algorithm would be particularly useful in the case of using RBFNs for online applications where the network configuration may need changes in time using a selfadjusting structure mechanism [58].…”

Section: B Rbf Network Designmentioning

confidence: 99%

“…The problem of the computational complexity of the RBF network design received a lot of attention and has been discussed by many researchers in the past (see, for example, [11], [28], [40], [46]), as well as in more current research (see, for example, [21], [43], [47], [58], [59]). Such a wide interest shows also that RBFNs design is still a lively area of research within the machine learning community and a search for approaches improving process of initialization and training of neural networks, including RBFNs, is still going on.…”

Section: Complexity Of the Rbf Network Designmentioning

confidence: 99%

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Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

2021

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Section: B Rbf Network Designmentioning

confidence: 99%

Section: B Rbf Network Designmentioning

confidence: 99%

Section: Complexity Of the Rbf Network Designmentioning

confidence: 99%

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Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

2021

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“…Therefore, to address the drawbacks in related researches, this research proposes an efficient high accuracy modeling scheme for spacecraft reaction wheel using RBFNN. Many researchers have proposed RBFNN as a modeling paradigm in different research areas [9][10][11][12][13][14][15]. For instance, in [9], RBFNN had been used for online modeling and adaptive control of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

A high accuracy modeling scheme for dynamic systems: spacecraft reaction wheel model

2022

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Reaction wheels are crucial actuators in spacecraft attitude control subsystem (ACS). The precise modeling of reaction wheels is of fundamental need in spacecraft ACS for design, analysis, simulation, and fault diagnosis applications. The complex nature of the reaction wheel leads to modeling difficulties utilizing the conventional modeling schemes. Additionally, the absence of reaction wheel providers’ parameters is crucial for triggering a new modeling scheme. The Radial Basis Function Neural Network (RBFNN) has an efficient architecture, alluring generalization properties, invulnerability against noise, and amazing training capabilities. This research proposes a promising modeling scheme for the spacecraft reaction wheel utilizing RBFNN and an improved variant of the Quantum Behaved Particle Swarm Optimization (QPSO). The problem of enhancing the network parameters of the RBFNN at the training phase is formed as a nonlinear constrained optimization problem. Thus, it is proposed to efficiently resolve utilizing an enhanced version of QPSO with mutation strategy (EQPSO-2M). The proposed technique is compared with the conventional QPSO algorithm and different variants of PSO algorithms. Evaluation criteria rely upon convergence speed, mean best fitness value, stability, and the number of successful runs that has been utilized to assess the proposed approach. A non-parametric test is utilized to decide the critical contrast between the results of the proposed algorithm compared with different algorithms. The simulation results demonstrated that the training of the proposed RBFNN-based reaction wheel model with enhanced parameters by EQPSO-2M algorithm furnishes a superior prediction accuracy went with effective network architecture.

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“…The coefficient of determination of the model and the predicted mean square error were 0.96 and 0.02, respectively, and the model was combined with the leaching process to optimize the leaching effect. Xie et al used a radial basis function neural network with a self-adjusting structure to effectively predict the iron ion concentration at the output of the hydrometallurgical zinc industry, and the root-mean-square error of the model prediction was 0.215 g/L, which compensated for the information delay caused by the inability to detect the iron ion concentration online in real time. In addition, random forests, decision trees, SVM, and extreme gradient boosting were also used in metallurgy and metal recycling. − …”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in a Mineral Leaching Process─Taking Pyrolusite Leaching as an Example

Zhang

et al. 2022

ACS Omega

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In this study, several machine learning models were used to analyze the process variables of electric-field-enhanced pyrolusite leaching and predict the leaching rate of manganese, and the applicability of those models in the leaching process of hydrometallurgy was compared. It showed that there was no correlation between the six leaching conditions; in addition to the leaching time, the concentrations of sulfuric acid and ferrous sulfate had great influences on the leaching of pyrolusite. The results of the prediction models showed that the support vector regression model has the best prediction performance, with regression index (R 2) = 0.92 and mean square error = 25.04, followed by the gradient boosting regression model (R 2 > 0.85). In this research, machine learning models were applied to the optimization of the manganese leaching process, and the research process and methods were also applicable to other hydrometallurgical processes for majorization and result prediction.

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On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network

Cited by 29 publications

References 41 publications

Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

A high accuracy modeling scheme for dynamic systems: spacecraft reaction wheel model

Application of Machine Learning in a Mineral Leaching Process─Taking Pyrolusite Leaching as an Example

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