Estimating ore grade using evolutionary machine learning models / / edited by Mohammad Ehteram [and three others]

• Pubblicazione: Gateway East, Singapore : , : Springer, , [2023]; ©2023
• Descrizione fisica: 1 online resource (109 pages)
• Disciplina: 006.31
• Soggetto topico: Machine learning; Ores - Sampling and estimation
• Persona (resp. second.): EhteramMohammad
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Contents -- List of Figures -- List of Tables -- 1 The Necessity of Grade Estimation -- 1.1 Introduction -- 1.1.1 The Importance of Ore Grade Estimation -- 1.2 Conventional Ore Grade Estimation Models -- 1.3 New Models for Estimating Ore Grade -- 1.4 General Remarks -- References -- 2 A Review of Modeling Approaches -- 2.1 Introduction -- 2.1.1 A Review of Studies of Applying MLMs for Estimating Ore Grade -- 2.2 Advantages and Disadvantages of Ore Grade Estimation Models -- 2.3 Shortcomings of Previous Studies -- 2.4 General Remarks -- References -- 3 Structure of Different Kinds of ANN Models -- 3.1 Introduction -- 3.1.1 A Review of Studies of Applying MLP, RBFNN, GMDH, and ELM for Estimating Different Variables in Geosciences and Mining Engineering, and Other Fields -- 3.2 Structure of Multi-Layer Perceptron Models -- 3.3 Structure of RBFNN Models -- 3.4 Structure of Extreme Learning Machine (ELM) Models -- 3.5 Structure of Group Method of Data Handling Neural Networks -- 3.6 General Remarks -- References -- 4 Optimization Algorithms and Classical Training Algorithms -- 4.1 Introduction -- 4.1.1 Backpropagation Algorithm -- 4.2 Levenberg-Marquardt Algorithm (LM) -- 4.3 Scaled Conjugate Gradient Algorithm -- 4.4 Variable Learning Rate Algorithm -- 4.5 Optimization Algorithm -- 4.5.1 Salp Swarm Algorithm (SSA) -- 4.5.2 Sine Cosine Algorithm (SCA) -- 4.5.3 Structure of Shark Swarm Optimization -- 4.5.4 Structure of Naked Mole-Rat (NMR) Algorithm -- 4.5.5 Structure of Particle Swarm Optimization -- 4.5.6 Structure of Genetic Algorithm for Solving Optimization Problems -- 4.6 Evolutionary Multi-Layer Perceptron (MLP) and Radial Basis Function Neural Network Models (RBFNN) -- 4.7 Evolutionary Extreme Learning Machine (ELM) -- 4.8 Evolutionary Group Method of Data Handling Neural Networks (GMDH) -- 4.9 General Remarks -- References. ; 5 Predicting Aluminum Oxide Grade -- 5.1 Introduction -- 5.1.1 Structure of Bayesian Model Averaging -- 5.2 Case Study -- 5.3 Results -- 5.3.1 Determination of Values of Random Parameters -- 5.3.2 Investigation of the Accuracy of Models for Predicting Ore Grade -- 5.3.3 Discussion -- 5.4 Conclusion -- References -- 6 Predicting Silicon Dioxide Grade -- 6.1 Introduction -- 6.1.1 Case Study -- 6.2 Results -- 6.2.1 Sensitivity Analysis for Choice of Algorithm Parameters -- 6.2.2 Investigation of the Accuracy of Models -- 6.3 Discussion -- 6.4 General Remarks -- References -- 7 Predicting Copper Ore Grade -- 7.1 Introduction -- 7.1.1 Kriging Method -- 7.2 Hybrid ELM and Kriging Method -- 7.3 Case Study -- 7.4 Part A -- 7.4.1 Results of Part A -- 7.5 Part B -- 7.5.1 Results for Part B -- 7.6 General Remarks -- References -- 8 Estimating Iron Ore Grade -- 8.1 Introduction -- 8.2 Part A -- 8.2.1 Case Study -- 8.2.2 Results -- 8.3 Part B -- 8.3.1 Generalized Likelihood Uncertainty Estimation -- 8.4 Analysis of Uncertainty Results -- 8.5 Discussion on the GMDH Models -- 8.6 General Remarks -- References -- 9 Conclusion and General Remarks for Estimating Ore Grade -- 9.1 Introduction -- 9.1.1 Final Results -- 9.1.2 Suggestions for the Future Studies -- References.
• Titolo autorizzato: Estimating ore grade using evolutionary machine learning models
• ISBN: 981-19-8106-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910637736603321
• Lo trovi qui: Univ. Federico II