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

巨大沒(méi)有

coalition

Neeta Sharma,Swati Sharma,Basant Prabhages and disadvantages of different models are described. This chapter presents the solutions for improving the accuracy of soft computing models. This chapter explains the details for quantifying uncertainty modeling. The chapter indicated that the artificial neural network models (ANN) had high cap

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Estimating Ore Grade Using Evolutionary Machine Learning Models

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Estimating Ore Grade Using Evolutionary Machine Learning Models978-981-19-8106-7

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The Necessity of Grade Estimation,elers need robust models for estimating ore grade since it is a nonlinear and complex process. We investigate the potential of different models for estimating ore grade. We explain the advantages and disadvantages of models. The purpose of this chapter is to assist modelers in choosing the best mode

討厭

A Review of Modeling Approaches,rent models. The chapter also discusses the benefits of different soft computing models. This chapter aims to assess the potential of artificial neural networks for estimating ore grades. In addition, this chapter examines the research gaps for estimating ore grade in previous studies. Additionally,

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Structure of Different Kinds of ANN Models, advanced operators. The advantages of each ANN model are discussed in this chapter. There are different layers in ANN models. Layers perform different tasks. The performance of ANN models depends on the parameters of ANNs. Different ANN models are compared for estimating ore grade in this chapter.