大葉大學圖書館 |

Handbook of research on predictive modeling and optimization methods in science and engineering[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	620.001/5196
書名/作者:	Handbook of research on predictive modeling and optimization methods in science and engineering/ Dookie Kim, Sanjiban Sekhar Roy, Tim Länsivaara, Ravinesh Deo, and Pijush Samui, editors.
其他題名:	Predictive modeling and optimization methods in science and engineering
其他作者:	Kim, Dookie,
出版者:	Hershey, Pennsylvania : : IGI Global,, [2018]
面頁冊數:	1 online resource (xxvi, 618 p.)
標題:	Engineering models.
標題:	System design.
標題:	Prediction theory - Mathematics.
標題:	Mathematical optimization.
ISBN:	9781522547679 (e-book)
ISBN:	9781522547662 (hardback)
書目註:	Includes bibliographical references and index.
內容註:	Chapter 1. A comparative study for locating critical failure surface in slope stability analysis via meta-heuristic approach -- Chapter 2. Adaptive refined-model-based approach for robust design optimization -- Chapter 3. An optimum tuning application of mass dampers considering soil-structure interaction: metaheuristic-based optimization of TMDs -- Chapter 4. Flood forecasting and uncertainty assessment using wavelet- and bootstrap-based neural networks -- Chapter 5. Grouping concept in optimum sizing of truss structures: optimization of truss structures -- Chapter 6. Hybrid data intelligent models and applications for water level prediction -- Chapter 7. Implementation of genetic-algorithm-based forecasting model to power system problems -- Chapter 8. Improvement of RSM prediction and optimization by using box-cox transformation: separation of colloidal contaminants from mineral processing effluents via electrocoagulation -- Chapter 9. Long-term degradation-based modeling and optimization framework -- Chapter 10. Multi-objective optimization of slope stability using wedge analysis and genetic algorithm -- Chapter 11. Multi-performance optimization in friction stir welding of aluminum alloy using response surface methodology -- Chapter 12. Multiscale modelling of daily suspended sediment load using MEMD-SLR coupled approach -- Chapter 13. Optimization of pile groups under vertical loads using metaheuristic algorithms -- Chapter 14. Optimization of the angle of twist of propeller using modified flower pollination algorithm -- Chapter 15. Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model -- Chapter 16. Optimum design of reinforced concrete retaining walls -- Chapter 17. Predicting human actions using a hybrid of relieff feature selection and kernel-based extreme learning machine -- Chapter 18. Predictive modeling and optimization of cutting forces through RSM and taguchi techniques in the turning of ASTM b574 (Hastelloy c-22) -- Chapter 19. Robust design of helicopter rotor flaps using bat algorithm -- Chapter 20. Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models -- Chapter 21. Soil cation exchange capacity predicted by learning from multiple modelling: forming multiple models run by SVM to learn from ANN and its hybrid with firefly algorithm -- Chapter 22. Usage of differential evolution algorithm in the calibration of parametric rainfall-runoff modeling -- Chapter 23. Whale optimization algorithm with wavelet mutation for the solution of optimal power flow problem.
摘要、提要註:	"This book explores the latest development of optimization techniques. It shows the application of optimization in new fields such as big data, artificial intelligence, etc. The application of hybrid optimization techniques and stochastic optimization are explored"--Provided by publisher.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-4766-2

Handbook of research on predictive modeling and optimization methods in science and engineering[electronic resource] /
Handbook of research on predictive modeling and optimization methods in science and engineering[electronic resource] /Predictive modeling and optimization methods in science and engineeringDookie Kim, Sanjiban Sekhar Roy, Tim Länsivaara, Ravinesh Deo, and Pijush Samui, editors. - Hershey, Pennsylvania :IGI Global,[2018] - 1 online resource (xxvi, 618 p.)

Includes bibliographical references and index.

Chapter 1. A comparative study for locating critical failure surface in slope stability analysis via meta-heuristic approach -- Chapter 2. Adaptive refined-model-based approach for robust design optimization -- Chapter 3. An optimum tuning application of mass dampers considering soil-structure interaction: metaheuristic-based optimization of TMDs -- Chapter 4. Flood forecasting and uncertainty assessment using wavelet- and bootstrap-based neural networks -- Chapter 5. Grouping concept in optimum sizing of truss structures: optimization of truss structures -- Chapter 6. Hybrid data intelligent models and applications for water level prediction -- Chapter 7. Implementation of genetic-algorithm-based forecasting model to power system problems -- Chapter 8. Improvement of RSM prediction and optimization by using box-cox transformation: separation of colloidal contaminants from mineral processing effluents via electrocoagulation -- Chapter 9. Long-term degradation-based modeling and optimization framework -- Chapter 10. Multi-objective optimization of slope stability using wedge analysis and genetic algorithm -- Chapter 11. Multi-performance optimization in friction stir welding of aluminum alloy using response surface methodology -- Chapter 12. Multiscale modelling of daily suspended sediment load using MEMD-SLR coupled approach -- Chapter 13. Optimization of pile groups under vertical loads using metaheuristic algorithms -- Chapter 14. Optimization of the angle of twist of propeller using modified flower pollination algorithm -- Chapter 15. Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model -- Chapter 16. Optimum design of reinforced concrete retaining walls -- Chapter 17. Predicting human actions using a hybrid of relieff feature selection and kernel-based extreme learning machine -- Chapter 18. Predictive modeling and optimization of cutting forces through RSM and taguchi techniques in the turning of ASTM b574 (Hastelloy c-22) -- Chapter 19. Robust design of helicopter rotor flaps using bat algorithm -- Chapter 20. Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models -- Chapter 21. Soil cation exchange capacity predicted by learning from multiple modelling: forming multiple models run by SVM to learn from ANN and its hybrid with firefly algorithm -- Chapter 22. Usage of differential evolution algorithm in the calibration of parametric rainfall-runoff modeling -- Chapter 23. Whale optimization algorithm with wavelet mutation for the solution of optimal power flow problem.

Restricted to subscribers or individual electronic text purchasers.

"This book explores the latest development of optimization techniques. It shows the application of optimization in new fields such as big data, artificial intelligence, etc. The application of hybrid optimization techniques and stochastic optimization are explored"--Provided by publisher.

ISBN: 9781522547679 (e-book)Subjects--Topical Terms:

553337
Engineering models.

LC Class. No.: TA177 / .H35 2018e

Dewey Class. No.: 620.001/5196

Handbook of research on predictive modeling and optimization methods in science and engineering[electronic resource] /
LDR:04192nmm a2200289 a 4500 001 513154
003 IGIG
005 20191022153520.0
006 m o d
007 cr cn
008 210929s2018 pau fob 001 0 eng d
010 $z 2017032864
020 $a 9781522547679 (e-book)
020 $a 9781522547662 (hardback)
035 $a (OCoLC)1038288028
035 $a 1081021242
040 $a CaBNVSL $b eng $c CaBNVSL $d CaBNVSL
050 0 0 $a TA177 $b .H35 2018e
082 0 0 $a 620.001/5196 $2 23
245 0 0 $a Handbook of research on predictive modeling and optimization methods in science and engineering $h [electronic resource] / $c Dookie Kim, Sanjiban Sekhar Roy, Tim Länsivaara, Ravinesh Deo, and Pijush Samui, editors.
246 3 0 $a Predictive modeling and optimization methods in science and engineering
260 $a Hershey, Pennsylvania : $b IGI Global, $c [2018]
300 $a 1 online resource (xxvi, 618 p.)
504 $a Includes bibliographical references and index.
505 0 $a Chapter 1. A comparative study for locating critical failure surface in slope stability analysis via meta-heuristic approach -- Chapter 2. Adaptive refined-model-based approach for robust design optimization -- Chapter 3. An optimum tuning application of mass dampers considering soil-structure interaction: metaheuristic-based optimization of TMDs -- Chapter 4. Flood forecasting and uncertainty assessment using wavelet- and bootstrap-based neural networks -- Chapter 5. Grouping concept in optimum sizing of truss structures: optimization of truss structures -- Chapter 6. Hybrid data intelligent models and applications for water level prediction -- Chapter 7. Implementation of genetic-algorithm-based forecasting model to power system problems -- Chapter 8. Improvement of RSM prediction and optimization by using box-cox transformation: separation of colloidal contaminants from mineral processing effluents via electrocoagulation -- Chapter 9. Long-term degradation-based modeling and optimization framework -- Chapter 10. Multi-objective optimization of slope stability using wedge analysis and genetic algorithm -- Chapter 11. Multi-performance optimization in friction stir welding of aluminum alloy using response surface methodology -- Chapter 12. Multiscale modelling of daily suspended sediment load using MEMD-SLR coupled approach -- Chapter 13. Optimization of pile groups under vertical loads using metaheuristic algorithms -- Chapter 14. Optimization of the angle of twist of propeller using modified flower pollination algorithm -- Chapter 15. Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model -- Chapter 16. Optimum design of reinforced concrete retaining walls -- Chapter 17. Predicting human actions using a hybrid of relieff feature selection and kernel-based extreme learning machine -- Chapter 18. Predictive modeling and optimization of cutting forces through RSM and taguchi techniques in the turning of ASTM b574 (Hastelloy c-22) -- Chapter 19. Robust design of helicopter rotor flaps using bat algorithm -- Chapter 20. Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models -- Chapter 21. Soil cation exchange capacity predicted by learning from multiple modelling: forming multiple models run by SVM to learn from ANN and its hybrid with firefly algorithm -- Chapter 22. Usage of differential evolution algorithm in the calibration of parametric rainfall-runoff modeling -- Chapter 23. Whale optimization algorithm with wavelet mutation for the solution of optimal power flow problem.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a "This book explores the latest development of optimization techniques. It shows the application of optimization in new fields such as big data, artificial intelligence, etc. The application of hybrid optimization techniques and stochastic optimization are explored"--Provided by publisher.
650 0 $a Engineering models. $3 553337
650 0 $a System design. $3 172186
650 0 $a Prediction theory $x Mathematics. $3 734476
650 0 $a Mathematical optimization. $3 176332
700 1 $a Kim, Dookie, $d 1969- $e editor. $3 690439
700 1 $a Roy, Sanjiban Sekhar, $e editor. $3 734473
700 1 $a Länsivaara, Tim, $e editor. $3 734474
700 1 $a Deo, Ravinesh, $e editor. $3 734475
700 1 $a Samui, Pijush, $d 1978- $e editor. $3 690437
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-4766-2