Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

Santosh, T.; Soni, Rahul; Eswaraiah, C.; Kumar, Shravan

doi:10.1016/j.apt.2022.103450

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…However, these methods usually have strict requirements for the dimension or domain mesh, the accuracy and computer running time can not be balanced at the same time. Back-Propagation Neural Network (BPNN) has been proved to be a viable, multipurpose and robust computational methodology with solid theoretic support and strong potential applications [24,25]. Many existing studies have shown the approximation ability of BPNN to nonlinear functions as well as the efficiency in solving solution of the nonlinear models [26].…”

Section: Introductionmentioning

confidence: 99%

Analytical and Numerical Methods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

Zhang,

Li,

Yang

et al. 2024

CMES

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The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model with noise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian white noise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. As follows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPT is obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrence of the tumor from the extinction state to the tumor-present state is more concerned in this paper. A more efficient algorithm of Back-Propagation Neural Network (BPNN) is utilized in order to testify the correction of the theoretical SPD and MFPT. With the existence of a weak signal, the functional relationship between Signal-to-Noise Ratio (SNR), noise intensities and correlation time is also studied. Numerical results show that both multiplicative Gaussian colored noise and additive Gaussian white noise can promote the extinction of the tumors, and the multiplicative Gaussian colored noise can lead to the resonance-like peak on MFPT curves, while the increasing intensity of the additive Gaussian white noise results in the minimum of MFPT. In addition, the correlation times are negatively correlated with MFPT. As for the SNR, we find the intensities of both the Gaussian white noise and the Gaussian colored noise, as well as their correlation intensity can induce SR. Especially, SNR is monotonously increased in the case of Gaussian white noise with the change of the correlation time. At last, the optimal parameters in BPNN structure are analyzed for MFPT from three aspects: the penalty factors, the number of neural network layers and the number of nodes in each layer.

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Section: Introductionmentioning

confidence: 99%

Analytical and Numerical Methods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

Zhang,

Li,

Yang

et al. 2024

CMES

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show abstract

“…In recent years, with the rapid development of artificial intelligent, neural networks as a powerful calculating tool, have been applied in diverse fields such as image processing, object detection, future prediction and so on. Among the different framework of neural networks, BPNN has been proved to be a viable, multipurpose and robust computational methodology with solid theoretic support and strong potential applications [26,27]. Many existing studies have shown the approximation ability of BPNN to nonlinear functions as well as the efficiency in solving solution to nonlinear models [28].…”

Section: Introductionmentioning

confidence: 99%

The Mean First-Passage Time and Stochastic Resonance of a Stochastic Tumor-immune Model via BPNN Method

Zhang

Yang

et al. 2023

Preprint

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The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model with noises perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian white noise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. As the follows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPT are obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrence of the tumor from the extinction state to the tumor-present state is more concerned in this paper. A higher efficient algorithm of Back-Propagation Neural Network (BPNN) is applied in order to testify the correction of the theoretic SPD and MFPT. With the existence of weak signal, the functional relationship between Signal-to-Noise Ratio (SNR), noise intensities and correlation time is also studied. These results show that both multiplicative Gaussian colored noise and additive Gaussian white noise can promote the extinction of tumors, and the multiplicative Gaussian colored noise will lead to the resonance-like peak on MFPT curves, while the increasing intensity of additive Gaussian white noise will cause the minimum of MFPT. In addition, the correlation times are negatively correlated with MFPT. As for the SNR, we find the intensities of Gaussian white noise and Gaussian colored noise, as well as their correlation intensity will induce SR. Especially, SNR is monotonously increased in the case of Gaussian white noise with the change of the correlation time, however, SNR displays the point of inflection in other cases. At last, the optimal parameters in BPNN structure are analyzed for MFPT from three aspects: the penalty factors, the number of neural network layers and the number of nodes in each layer.

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“…Graduate School of Engineering, Nagoya University (Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan) Corresponding Authors *c_takai@gifu-u.ac.jp **yamashita.seiji@material.nagoya-u.ac.jp 材料設計において用いられる機械学習として，人工ニューラルネットワーク(ANN) ，深層学習(DNN) ，サポートベクターマシン(SVM) ，主成分分析(PCA) ，決定木(デシジョンツリー) ，ランダムフォレストなどが報告されている。粉体工学分野においても機械学習法は，粒子製造プロセスの最適化や種々機能性の最大化 [3][4][5]，未知試料の評価 [6,7]…”

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Sex Determination of 3<sup>rd</sup>-instar Larva of Japanese Rhinoceros Beetle Based on Their Droppings Using Mahalanobis-Taguchi System

et al. 2022

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The Mahalanobis-Taguchi system (MTS) is one of the multi-variate analyses which can classify data by correlations among multi-variables. The MTS has significant advantage of applying a cause analysis which provides effective variable combinations to upgrade the accuracy of the classification. In this study, droppings, which were extruded from Japanese rhinoceros beetle larva were selected to classify into male/female based on their shape-related features using the MTS. Based on the cause analysis, the projected area/Feret's diameter (A/F) and solidity were the best variable combination which all the larval droppings were classified into male/female. Both the variable has surface roughness-related factors with size effect (A/F) without size effect (solidity). The MTS has the great ability to classify from the powders' minor difference and will be an effective tool for particle design in the near future.

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Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

Cited by 9 publications

References 38 publications

Analytical and Numerical Methods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

Analytical and Numerical Methods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

The Mean First-Passage Time and Stochastic Resonance of a Stochastic Tumor-immune Model via BPNN Method

Sex Determination of 3<sup>rd</sup>-instar Larva of Japanese Rhinoceros Beetle Based on Their Droppings Using Mahalanobis-Taguchi System

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