標(biāo)題: Titlebook: Estimating Ore Grade Using Evolutionary Machine Learning Models; Mohammad Ehteram,Zohreh Sheikh Khozani,Maliheh Abb Book 2023 The Editor(s [打印本頁(yè)] 作者: EXTRA 時(shí)間: 2025-3-21 17:09
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書目名稱Estimating Ore Grade Using Evolutionary Machine Learning Models讀者反饋
書目名稱Estimating Ore Grade Using Evolutionary Machine Learning Models讀者反饋學(xué)科排名
作者: Innocence 時(shí)間: 2025-3-21 21:53 作者: antidepressant 時(shí)間: 2025-3-22 02:43
Predicting Aluminum Oxide Grade,, MLP-NMR, MLP-SSA, MLP-PSO, MLP-GA, and MLP models were 10.12, 10.98, 11.12, 12.23, 14.45, and 15.56 at the testing level. The Nash Sutcliffe efficiencies (NSE) of the BMA, MLP-NMR, MLP-SSA, MLP-PSO, MLP-GA, and MLP models were 0.94, 0.92, 0.89, 0.86, 0.84, and 0.82 at the testing level.作者: Fibrillation 時(shí)間: 2025-3-22 05:43 作者: 低位的人或事 時(shí)間: 2025-3-22 10:56 作者: Interdict 時(shí)間: 2025-3-22 16:58 作者: Interdict 時(shí)間: 2025-3-22 17:55 作者: Intellectual 時(shí)間: 2025-3-22 21:14 作者: 搖曳的微光 時(shí)間: 2025-3-23 03:50
Estimating Iron Ore Grade,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.作者: seruting 時(shí)間: 2025-3-23 07:07 作者: Ligament 時(shí)間: 2025-3-23 12:17 作者: 巧思 時(shí)間: 2025-3-23 16:00
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.作者: VEST 時(shí)間: 2025-3-23 22:02 作者: 親密 時(shí)間: 2025-3-23 23:29 作者: Somber 時(shí)間: 2025-3-24 04:40 作者: armistice 時(shí)間: 2025-3-24 07:28
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.作者: syring 時(shí)間: 2025-3-24 12:45
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.作者: 項(xiàng)目 時(shí)間: 2025-3-24 17:31
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.作者: thwart 時(shí)間: 2025-3-24 21:49
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.作者: 字形刻痕 時(shí)間: 2025-3-25 01:03
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.作者: 惡意 時(shí)間: 2025-3-25 04:41
https://doi.org/10.1007/978-981-19-8106-7Ore Grade Estimation; Machine Learning Models; Optimization Algorithms; Ensemble Models; Bayesian model; 作者: 魔鬼在游行 時(shí)間: 2025-3-25 09:04 作者: Itinerant 時(shí)間: 2025-3-25 13:38 作者: Misnomer 時(shí)間: 2025-3-25 19:34
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作者: foppish 時(shí)間: 2025-3-25 23:10 作者: conception 時(shí)間: 2025-3-26 00:31
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. 作者: 星球的光亮度 時(shí)間: 2025-3-26 08:14
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作者: 跳動(dòng) 時(shí)間: 2025-3-26 09:02
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作者: mettlesome 時(shí)間: 2025-3-26 16:19
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作者: Morose 時(shí)間: 2025-3-26 19:12 作者: 巨大沒有 時(shí)間: 2025-3-26 23:06 作者: coalition 時(shí)間: 2025-3-27 04:23
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作者: 食草 時(shí)間: 2025-3-27 08:57 作者: BIDE 時(shí)間: 2025-3-27 10:44 作者: CREST 時(shí)間: 2025-3-27 16:54 作者: Arable 時(shí)間: 2025-3-27 18:08
Estimating Ore Grade Using Evolutionary Machine Learning Models作者: 極端的正確性 時(shí)間: 2025-3-27 22:37
Estimating Ore Grade Using Evolutionary Machine Learning Models978-981-19-8106-7作者: 誘使 時(shí)間: 2025-3-28 03:42
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作者: 討厭 時(shí)間: 2025-3-28 07:46
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,作者: Dissonance 時(shí)間: 2025-3-28 13:29
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. 作者: 容易做 時(shí)間: 2025-3-28 14:40 作者: 冷峻 時(shí)間: 2025-3-28 21:39 作者: NAV 時(shí)間: 2025-3-28 23:08
