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

惡意

https://doi.org/10.1007/978-981-19-8106-7Ore Grade Estimation; Machine Learning Models; Optimization Algorithms; Ensemble Models; Bayesian model;

魔鬼在游行

Itinerant

Misnomer

Crop Improvement Under Adverse Conditionselers 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

foppish

conception

Faheema Khan,Khalid Rehman Hakeem 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.

星球的光亮度

Abazar Rajabi,Eric Schmieder Ober. The different optimization algorithms are reliable tools for training ANN models. These algorithms use advanced operators to train the ANN models. The ANN parameters, such as bias and weight, are unknown. Thus, robust optimization algorithms can be used for adjusting ANN parameters. This chapter e

跳動(dòng)

Crop Production under Stressful Conditionsand depth of boreholes are used for estimating aluminum oxide grade. In the first level, the multi-layer perceptron (MLP)–particle swarm optimization (PSO), MLP–salp swarm algorithm (SSA), MLP–naked mole rate algorithm (NMR), MLP–genetic algorithm (MLP-GA), and MLP are used for estimating aluminum o

mettlesome

Annie Ruttledge,Bhagirath S. Chauhaned using the firefly algorithm (FFA), shark smell optimization (SSO), and sine cosine algorithm (SCA). An inclusive multiple model was built by integrating the outputs of RBFNN-FFA, RBFNN-SSO, RBFNN-SCA, and RBFNN. At the training level, the mean absolute error (MAE) of the IMM was 7.89, while it wa

Morose