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