Titlebook: Estimating Ore Grade Using Evolutionary Machine Learning Models; Mohammad Ehteram,Zohreh Sheikh Khozani,Maliheh Abb Book 2023 The Editor(s

Ligament

巧思

Faheema Khan,Khalid Rehman Hakeemt tasks. The performance of ANN models depends on the parameters of ANNs. Different ANN models are compared for estimating ore grade in this chapter. A modeler can choose the best ANN model by understanding its different features.

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親密

Somber

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https://doi.org/10.1007/978-94-011-1490-5 this chapter suggests solutions to improve the accuracy of models for estimating ore grades. This chapter examines the drawbacks of different models. The chapter indicated that ore grade could be accurately estimated using soft computing models.

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Abazar Rajabi,Eric Schmieder Oberxplains the structure of different optimization algorithms for solving optimization problems. The advantages and disadvantages of different optimization algorithms are explained in this chapter. The optimization algorithms use advanced operators to adjust the ANN parameters.

項(xiàng)目

Annie Ruttledge,Bhagirath S. Chauhans 8.12, 8.25, 8.57, and 8.98 for the RBFNN-SSO, RBFNN-SCA, RBFNN-FFA, and RBFNN. At the testing level, the IMM decreased the MAE of the RBFNN-SSO, RBFNN-SCA, RBFNN-FFA, and RFBNN by 0.9, 8.5, 17, and 20%, respectively. The results indicated that the IMM model was reliable for estimating ore grade.

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Crop Rotation Defeats Pests and Weeds,FA, GMDH-PSO, GMDH-GA, and GMDH were 4.55, 5.12, 5.54, 5.89, and 5.91. At the testing level, the GMDH-SSA decreased the MAE of the GMDH-SCA, GMDH-FFA, GMDH-PSO, GMDH-GA, and GMDH by 4.7, 14, 16, and 17%, respectively. The optimized GMDH models had a high potential for estimating iron ore grade.

字形刻痕

Neeta Sharma,Swati Sharma,Basant Prabhaabilities for estimating ore grades. This chapter indicated that the model parameters and input parameters are the uncertainty resources in the modeling process. Also, the optimization algorithms improved the accuracy of ANN models for estimating ore grade.