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1. Full Text Application of back-propagation networks in debris flow prediction
by Chang, Tung-Chueng and Chao, Ru-Jen
Engineering Geology, ISSN 0013-7952, 2006, Volume 85, Issue 3, pp. 270 - 280
Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought... 
Debris flows | Back-propagation | Artificial neural networks | Taiwan | ENGINEERING, GEOLOGICAL | artificial neural networks | debris flows | GEOSCIENCES, MULTIDISCIPLINARY | back-propagation | Water use | Reservoirs | Water pollution | Algorithms | Neural networks | Rain and rainfall
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2. Full Text A combination strategy of random forest and back propagation network for variable selection in spectral calibration
by Chen, Huazhou and Liu, Zhenyao and Liu, Xiaoke and Jia, Zhen and Shi, Kai and Cai, Ken
Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, 11/2018, Volume 182, pp. 101 - 108
Random forest (RF) and neural network have received significant interest for statistical data analysis as a result of their good predictive performance and... 
Random forest | Gini index | Fourier transform near infrared spectroscopy | Back propagation network | Soil organic carbon | Variable selection | REGRESSION | CHEMISTRY, ANALYTICAL | CLASSIFICATION | STATISTICS & PROBABILITY | QUANTITATIVE-ANALYSIS | GASOLINE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ARTIFICIAL NEURAL-NETWORKS | PREDICTION | INSTRUMENTS & INSTRUMENTATION | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | NIR | NEAR-INFRARED SPECTROSCOPY | OPTIMIZATION | REFLECTANCE SPECTROSCOPY | AUTOMATION & CONTROL SYSTEMS | Soils | Usage | Carbon content | Neural networks | Analysis
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3. Retraction notice to ``Using a back propagation network combined with grey clustering to forecast policyholder decision to purchase investment-inked insurance'' [ESWA 38/6 (2010) 6736-6747]
by Ker-Tah Hsu
Expert Systems with Applications, ISSN 0957-4174, 03/2019, Volume 117, p. 367
Clustering | Back propagation networks
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4. Dynamic cloud back-propagation networks and its application in fault diagnostic for liquid-propellant rocket engines
by Nie, Yao and Cheng, Yuqiang and Wu, Jianjun
Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, ISSN 0954-4100, 03/2018, Volume 232, Issue 3, pp. 583 - 594
Fault diagnosis for liquid-propellant rocket engines often faces a lack of prior knowledge or insufficient sampling data, and thus becomes a decision-making... 
fault diagnosis | failure mode | neural network | Liquid-propellant rocket engines | cloud model | ENGINEERING, AEROSPACE | ENGINEERING, MECHANICAL | Random noise | Propagation | Computer simulation | Decision making | Failure modes | Back propagation | Rocket engines | Back propagation networks | Information sources | Engines | Fault diagnosis | Fault detection | Neural networks | Diagnostic software | Randomness | Feature extraction | Diagnostic systems
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5. Full Text Comparative study of probabilistic neural network and back propagation network for fault diagnosis of refrigeration systems
by Liang, Qingqing and Han, Hua and Cui, Xiaoyu and Qing, Hong and Fan, Yuqiang
Science and Technology for the Built Environment, ISSN 2374-4731, 04/2018, Volume 24, Issue 4, pp. 448 - 457
Fault diagnosis is of crucial importance to mechanical systems. To find an efficient way of conducting fault diagnosis for refrigeration systems, probabilistic... 
THERMODYNAMICS | CONSTRUCTION & BUILDING TECHNOLOGY | PERFORMANCE | STRATEGY | ENGINEERING, MECHANICAL
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6. Full Text A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting
by Madhiarasan, M and Deepa, S.N
Applied Intelligence, ISSN 0924-669X, 06/2016, Volume 44, Issue 4, pp. 878 - 893
To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: http://dx.doi.org/10.1007/s10489-015-0737-z 
Improved back propagation networks | Wind speed | Forecasting | Hidden neurons | Novel criterion | Neurons | Neural networks | Analysis | Fittings | Computer simulation | Artificial neural networks | Criteria | Back propagation networks
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7. Full Text A simulated-annealing-based approach for simultaneous parameter optimization and feature selection of back-propagation networks
by Lin, Shih-Wei and Tseng, Tsung-Yuan and Chou, Shuo-Yan and Chen, Shih-Chieh
Expert Systems With Applications, ISSN 0957-4174, 2008, Volume 34, Issue 2, pp. 1491 - 1499
The back-propagation network (BPN) can be used in various fields. Nevertheless, different problems may require different parameter settings for network... 
Simulated-annealing | Feature selection | Optimization | Back-propagation network | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | optimization | MULTILAYER PERCEPTRONS | GLOBAL OPTIMIZATION | back-propagation network | FEATURE SUBSET-SELECTION | simulated-annealing | feature selection | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ARTIFICIAL NEURAL-NETWORKS | GENETIC ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC
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8. Full Text Fuzzy back-propagation network approach for estimating the simulation workload
by Toly Chen, Tin-Chih
Neural Computing and Applications, ISSN 0941-0643, 8/2016, Volume 27, Issue 6, pp. 1707 - 1715
Estimating the time required for simulating a factory online is a crucial topic in manufacturing. However, this topic has rarely been discussed. A fuzzy... 
Computational Biology/Bioinformatics | Simulation | Computer Science | Data Mining and Knowledge Discovery | Image Processing and Computer Vision | Artificial Intelligence (incl. Robotics) | Workload estimation | Fuzzy | Computational Science and Engineering | Probability and Statistics in Computer Science | Back-propagation network | SYSTEM | DUE-DATE ASSIGNMENT | TIME | PARAMETERS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | PREDICTION | NEURAL-NETWORKS | TAGUCHIS DESIGN | WAFER FABRICATION PLANT
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9. Full Text Back propagation neural network with adaptive differential evolution algorithm for time series forecasting
by Wang, Lin and Zeng, Yi and Chen, Tao
Expert Systems With Applications, ISSN 0957-4174, 02/2015, Volume 42, Issue 2, pp. 855 - 863
The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the... 
Time series forecasting | Back propagation neural network | Differential evolution algorithm | DEMAND | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | PROJECTS | ARIMA | OPTIMIZATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | HYBRID MODELS | ENGINEERING, ELECTRICAL & ELECTRONIC | Neural networks | Algorithms | Thresholds | Searching | Adaptive algorithms | Artificial neural networks | Back propagation | Mathematical models | Forecasting
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10. Full Text Forecasting stock indices with back propagation neural network
by Wang, Jian-Zhou and Wang, Ju-Jie and Zhang, Zhe-George and Guo, Shu-Po
Expert Systems With Applications, ISSN 0957-4174, 2011, Volume 38, Issue 11, pp. 14346 - 14355
► We propose a new approach to forecasting the stock prices. ► We utilize the Wavelet De-noising-based Back Propagation (WDBP) neural network. ► An effective... 
Stock prices | BP neural network | Wavelet de-noising | WDBP neural network | SYSTEM | MARKET | MODEL | ALGORITHMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | PREDICTION | ENGINEERING, ELECTRICAL & ELECTRONIC | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | GARCH | SELECTION | Stock price indexes | Neural networks | Algorithms | Time series | Back propagation | Markets | Raw materials | Forecasting | Expert systems
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11. Full Text Trainable cascade-forward back-propagation network modeling of spearmint oil extraction in a packed bed using SC-CO2
by Lashkarbolooki, Mostafa and Shafipour, Zeinab Sadat and Hezave, Ali Zeinolabedini
The Journal of Supercritical Fluids, ISSN 0896-8446, 01/2013, Volume 73, pp. 108 - 115
► A CFBPN model was developed to correlate the extraction yield of spearmint oil. ► Proposed method is straightforward, efficient and quick compared to... 
Packed bed | Extraction | Artificial neural network modeling | Spearmint | SC-CO2 | SC-CO | CHEMISTRY, PHYSICAL | ARTIFICIAL NEURAL-NETWORK | SIMULATION | SUPERCRITICAL-FLUID EXTRACTION | ENGINEERING, CHEMICAL | CONDUCTIVITY | PREDICT | CO2 EXTRACTION | SEED OIL | CARBON-DIOXIDE EXTRACTION | IONIC LIQUIDS | Networks | Supercritical fluids | Mathematical analysis | Solubility | Carbon dioxide | Back propagation | Mathematical models
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12. Full Text A nonlinearly normalized back propagation network and cloud computing approach for determining cycle time allowance during wafer fabrication
by Chen, Toly and Wang, Yi-Chi
Robotics and Computer Integrated Manufacturing, ISSN 0736-5845, 06/2017, Volume 45, pp. 144 - 156
This study investigated the determination of the allowance that must be added to the cycle time estimate, which is a critical concern when assigning internal... 
Internal due date assignment | Allowance determination | Wafer fabrication | Upper bound | Back propagation network | SYSTEM | COMPETITIVENESS | ROBOTICS | DUE-DATE ASSIGNMENT | SUSTAINABILITY | PREDICTION | SOM | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | NEURAL-NETWORKS | TAGUCHIS DESIGN | ENGINEERING, MANUFACTURING | RULES | Computer organization | Cloud computing
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13. A nonlinearly normalized back propagation network and cloud computing approach for determining cycle time allowance during wafer fabrication
by Toly Chen and Yi-Chi Wang
Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, 06/2017, Volume 45, p. 144
This study investigated the determination of the allowance that must be added to the cycle time estimate, which is a critical concern when assigning internal... 
Cloud computing | Upper bounds | Estimation | Back propagation | Computing time | Back propagation networks | Nonlinear systems | Cycle time | Due dates
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14. Full Text Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network
by Wang, Yu-Cheng
Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, 2018
Engine failure time | Classification and regression tree | Back propagation network | Principal component analysis
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15. Full Text Multivariate Coupling Sensitivity Analysis Method Based on a Back-Propagation Network and Its Application
by Li, Hongyan and Tian, Qi and Wang, Xiaojun and Wu, Ya’nan
Journal of Hydrologic Engineering, ISSN 1084-0699, 08/2015, Volume 20, Issue 8, p. 6014013
AbstractSensitivity analysis is a technique for quantitatively analyzing the variation in a dependent variable caused by each independent variable. In this... 
Technical Notes | Technical Note | Global sensitivity analysis | Nonlinear | Computer simulation | Relation mapping | Back propagation network | Runoff | OUTPUT | WATER RESOURCES | ENVIRONMENTAL SCIENCES | ENGINEERING, CIVIL | MODELS | INPUT VARIABLES | SYSTEMS
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16. Integrating decision tree with back propagation network to conduct business diagnosis and performance simulation for solar companies
by Chih-Hsuan Wang and Jia-Jien Chuang
Decision Support Systems, ISSN 0167-9236, 01/2016, Volume 81, p. 12
  Solar energy is a natural, clean, and inexhaustible resource to help the earth solve its energy crisis and reduce the air pollution caused by coal, nuclear,... 
Photovoltaic cells | Solar energy | Back propagation | Competitive intelligence | Decision trees | Balanced Scorecard | Validation studies
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17. Full Text Applying back propagation network to cold chain temperature monitoring
by Chen, Kai-Ying and Shaw, Yi-Cheng
Advanced Engineering Informatics, ISSN 1474-0346, 2011, Volume 25, Issue 1, pp. 11 - 22
Recently, the enhancement of contactless, real-time features and high data transmission rates in supply chain management has been widely discussed. Notably,... 
Statistical process control | Temperature monitoring | Back propagation network | EWMA | Radio frequency identification | ENGINEERING, MULTIDISCIPLINARY | MODEL | CONTROL SCHEMES | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Computer simulation | Neural networks | Supply chains | Monitors | Chains | Collection | Management
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18. Full Text Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network
by Zeng, Yu-Rong and Zeng, Yi and Choi, Beomjin and Wang, Lin
Energy, ISSN 0360-5442, 05/2017, Volume 127, pp. 381 - 396
Reliable energy consumption forecasting can provide effective decision-making support for planning development strategies to energy enterprises and for... 
Multifactor-influenced forecasting | Back-propagation neural network | Energy consumption | Mean impact value | Adaptive differential evolution | SUPPORT VECTOR REGRESSION | ENERGY & FUELS | COLONY OPTIMIZATION APPROACH | ELECTRICITY CONSUMPTION | MODEL | PARTICLE SWARM OPTIMIZATION | DEMAND | DIFFERENTIAL EVOLUTION ALGORITHM | THERMODYNAMICS | JOINT REPLENISHMENT PROBLEM | TIME-SERIES | Energy use | Business schools | Algorithms | Neural networks | Analysis | Energy policy | Forecasts and trends
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19. Full Text Assessment of dysarthric speech using Elman back propagation network (recurrent network) for speech recognition
by Selva Nidhyananthan, S and Shantha Selva kumari, R and Shenbagalakshmi, V
International Journal of Speech Technology, ISSN 1381-2416, 9/2016, Volume 19, Issue 3, pp. 577 - 583
In this paper, the role of speech recognition system in the assessment of dysarthric speech based on a method called Elman back propagation network (EBN) is... 
EBN | Engineering | Glottal features | Signal, Image and Speech Processing | Neurological disability | Speech intelligibility | Artificial Intelligence (incl. Robotics) | Social Sciences, general | Dysarthria | Voice recognition | Algorithms | Neural networks | Analysis | Propagation | Intelligibility | Back propagation | Back propagation networks | Speech recognition | Speech | Voice disorders | Nervous system disorders | Damage | Speech disorders | Recognition | Networks | Databases | Mathematical models | Assessments
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20. Quality prediction of laser milling based on optimized back propagation networks by genetic algorithms