Back propagation neural network with adaptive differential evolution algorithm for time series forecasting
•We propose a BPNN with adaptive differential evolution (ADE) for time series forecasting.•ADE is used to search for global initial connection weights and thresholds of BPNN.•The proposed ADE–BPNN is effective for improving forecasting accuracy. The back propagation neural network (BPNN) can easily...
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| Veröffentlicht in: | Expert systems with applications Jg. 42; H. 2; S. 855 - 863 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
01.02.2015
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| Schlagworte: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online-Zugang: | Volltext |
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| Abstract | •We propose a BPNN with adaptive differential evolution (ADE) for time series forecasting.•ADE is used to search for global initial connection weights and thresholds of BPNN.•The proposed ADE–BPNN is effective for improving forecasting accuracy.
The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE–BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE–BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models. |
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| AbstractList | The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE-BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE-BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models. •We propose a BPNN with adaptive differential evolution (ADE) for time series forecasting.•ADE is used to search for global initial connection weights and thresholds of BPNN.•The proposed ADE–BPNN is effective for improving forecasting accuracy. The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the adaptive differential evolution (ADE) algorithm with BPNN, called ADE–BPNN, is designed to improve the forecasting accuracy of BPNN. ADE is first applied to search for the global initial connection weights and thresholds of BPNN. Then, BPNN is employed to thoroughly search for the optimal weights and thresholds. Two comparative real-life series data sets are used to verify the feasibility and effectiveness of the hybrid method. The proposed ADE–BPNN can effectively improve forecasting accuracy relative to basic BPNN, autoregressive integrated moving average model (ARIMA), and other hybrid models. |
| Author | Zeng, Yi Chen, Tao Wang, Lin |
| Author_xml | – sequence: 1 givenname: Lin surname: Wang fullname: Wang, Lin email: wanglin982@gmail.com organization: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China – sequence: 2 givenname: Yi surname: Zeng fullname: Zeng, Yi email: zengy200810@126.com organization: School of Management, Huazhong University of Science and Technology, Wuhan 430074, China – sequence: 3 givenname: Tao surname: Chen fullname: Chen, Tao email: chentao15@163.com organization: College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China |
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| Cites_doi | 10.1016/j.eswa.2011.02.046 10.1016/S0305-0548(99)00123-9 10.1016/j.enpol.2006.05.009 10.1016/j.ejor.2003.08.037 10.1155/2014/614342 10.1108/MD-08-2013-0430 10.1155/2013/270249 10.1155/2013/125893 10.1016/S0045-7825(01)00372-3 10.2307/2345277 10.1016/j.ijproman.2014.01.012 10.1016/j.phrp.2013.10.009 10.1016/j.fuel.2014.02.034 10.1155/2013/309750 10.1016/j.eswa.2013.08.078 10.1016/j.eswa.2012.08.012 10.1016/S0925-2312(01)00702-0 10.1016/S0169-2070(97)00044-7 10.1016/j.medengphy.2005.06.006 10.1016/j.neucom.2008.04.017 10.1080/18756891.2013.809937 10.1007/11758501_97 10.1016/j.apenergy.2012.01.063 10.1002/for.1218 10.1016/j.ejor.2004.08.043 10.1016/j.energy.2012.07.006 10.1016/j.eswa.2013.04.013 10.1016/j.enpol.2009.06.046 10.1198/073500102753410444 10.1155/2014/675721 10.1016/j.apm.2013.05.016 10.1016/j.eswa.2011.09.157 10.1007/s10462-009-9137-2 10.1016/j.amc.2006.07.025 10.1016/j.tourman.2007.04.003 10.1016/S0925-2312(99)00127-7 10.1023/A:1008202821328 10.1111/j.1468-0394.2011.00594.x 10.1016/j.enpol.2009.04.049 10.1016/j.dss.2009.02.001 10.1080/10629360600564874 10.1016/j.enconman.2005.02.004 10.1016/j.knosys.2013.09.013 10.1016/S0003-682X(98)00078-4 10.1016/j.eswa.2011.09.116 |
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| References | Diebold, Mariano (b0045) 2002; 20 Kumar, Jain (b0090) 1999; 58 Chen, Fu, Liang, Sema, Li, Tontiwachwuthikul (b0030) 2014; 126 Bennett, Stewart, Beal (b0015) 2013; 40 Ticknor (b0150) 2013; 40 Wang, Fu, Zeng (b0165) 2012; 39 Neri, Tirronen (b0125) 2010; 33 Irani, Nasimi (b0065) 2011; 38 Lu, Lee, Chiu (b0115) 2009; 47 Pai, Hong (b0135) 2005; 46 Cui, Wang, Deng, Zhang (b9000) 2013; 2013 Wang, Qu, Chen, Yan (b0175) 2013; 2013 Adebiyi, Adewumi, Ayo (b0005) 2014; 2 Box, Jenkins (b0020) 1976 Cui, Wang, Deng (b0040) 2014; 41 Zhang, Patuwo, Hu (b0225) 2001; 28 Matias, Reboredo (b0120) 2012; 31 Yam, Chow (b0195) 2000; 30 Wang (b0160) 2013; 29 Ediger, Akar (b0050) 2007; 35 Liu, Wang (b0105) 2014; 32 Lee, Tong (b0095) 2012; 94 Wang, Zhu, Zhang, Sun (b0190) 2009; 37 Li, Guo, Wang, Fu (b0100) 2013; 2013 Hosseini, Luo, Reynolds (b0060) 2006; 28 Zhang, Patuwo, Hu (b0220) 1998; 14 Khashei, Bijari (b0075) 2012; 39 Liu, Wang (b0110) 2014; 52 Zhang, Zhang, Lok, Lyu (b0240) 2007; 185 Aslanargun, Mammadov, Yazici, Yolacan (b0010) 2007; 77 Ju, Hong (b0070) 2013; 37 Khashei, Rafiei, Bijari (b0085) 2013; 6 Onwubolu, Davendra (b0130) 2006; 171 Zeng, Wang, Xu, Fu (b0205) 2014; 2014 Zhang (b0210) 2003; 50 Qu, Wang, Zeng (b0140) 2013; 54 Storn, Price (b0145) 1997; 11 Wang, Qu, Liu, Chen (b0180) 2014; 2014 Campbell, Walker (b0025) 1977; 140 Khashei, Bijari, Ardali (b0080) 2009; 72 Chu (b0035) 2008; 29 Wang, Zeng, Zhang, Huang, Bao (b0185) 2006; 3991 Geem, Roper (b0055) 2009; 37 Vesterstrom, Thomsen (b0155) 2004; 2 Zhang, Hong, Dong, Tsai, Sung, Fan (b0215) 2012; 45 Yu, Kim, Kim (b0200) 2013; 4 Zhang, Subbarayan (b0235) 2002; 191 Wang, He, Zeng (b0170) 2012; 29 Zhang, Qi (b0230) 2005; 160 Adebiyi (10.1016/j.eswa.2014.08.018_b0005) 2014; 2 Neri (10.1016/j.eswa.2014.08.018_b0125) 2010; 33 Pai (10.1016/j.eswa.2014.08.018_b0135) 2005; 46 Zhang (10.1016/j.eswa.2014.08.018_b0215) 2012; 45 Matias (10.1016/j.eswa.2014.08.018_b0120) 2012; 31 Qu (10.1016/j.eswa.2014.08.018_b0140) 2013; 54 Irani (10.1016/j.eswa.2014.08.018_b0065) 2011; 38 Liu (10.1016/j.eswa.2014.08.018_b0105) 2014; 32 Box (10.1016/j.eswa.2014.08.018_b0020) 1976 Lu (10.1016/j.eswa.2014.08.018_b0115) 2009; 47 Cui (10.1016/j.eswa.2014.08.018_b0040) 2014; 41 Li (10.1016/j.eswa.2014.08.018_b0100) 2013; 2013 Ediger (10.1016/j.eswa.2014.08.018_b0050) 2007; 35 Wang (10.1016/j.eswa.2014.08.018_b0185) 2006; 3991 Liu (10.1016/j.eswa.2014.08.018_b0110) 2014; 52 Wang (10.1016/j.eswa.2014.08.018_b0170) 2012; 29 Wang (10.1016/j.eswa.2014.08.018_b0160) 2013; 29 Zhang (10.1016/j.eswa.2014.08.018_b0225) 2001; 28 Onwubolu (10.1016/j.eswa.2014.08.018_b0130) 2006; 171 Vesterstrom (10.1016/j.eswa.2014.08.018_b0155) 2004; 2 Wang (10.1016/j.eswa.2014.08.018_b0180) 2014; 2014 Aslanargun (10.1016/j.eswa.2014.08.018_b0010) 2007; 77 Storn (10.1016/j.eswa.2014.08.018_b0145) 1997; 11 Zhang (10.1016/j.eswa.2014.08.018_b0230) 2005; 160 Geem (10.1016/j.eswa.2014.08.018_b0055) 2009; 37 Chen (10.1016/j.eswa.2014.08.018_b0030) 2014; 126 Zhang (10.1016/j.eswa.2014.08.018_b0210) 2003; 50 Khashei (10.1016/j.eswa.2014.08.018_b0085) 2013; 6 Lee (10.1016/j.eswa.2014.08.018_b0095) 2012; 94 Yam (10.1016/j.eswa.2014.08.018_b0195) 2000; 30 Zhang (10.1016/j.eswa.2014.08.018_b0240) 2007; 185 Zhang (10.1016/j.eswa.2014.08.018_b0220) 1998; 14 Zhang (10.1016/j.eswa.2014.08.018_b0235) 2002; 191 Zeng (10.1016/j.eswa.2014.08.018_b0205) 2014; 2014 Wang (10.1016/j.eswa.2014.08.018_b0190) 2009; 37 Kumar (10.1016/j.eswa.2014.08.018_b0090) 1999; 58 Yu (10.1016/j.eswa.2014.08.018_b0200) 2013; 4 Diebold (10.1016/j.eswa.2014.08.018_b0045) 2002; 20 Khashei (10.1016/j.eswa.2014.08.018_b0080) 2009; 72 Campbell (10.1016/j.eswa.2014.08.018_b0025) 1977; 140 Chu (10.1016/j.eswa.2014.08.018_b0035) 2008; 29 Bennett (10.1016/j.eswa.2014.08.018_b0015) 2013; 40 Ticknor (10.1016/j.eswa.2014.08.018_b0150) 2013; 40 Ju (10.1016/j.eswa.2014.08.018_b0070) 2013; 37 Cui (10.1016/j.eswa.2014.08.018_b9000) 2013; 2013 Khashei (10.1016/j.eswa.2014.08.018_b0075) 2012; 39 Hosseini (10.1016/j.eswa.2014.08.018_b0060) 2006; 28 Wang (10.1016/j.eswa.2014.08.018_b0165) 2012; 39 Wang (10.1016/j.eswa.2014.08.018_b0175) 2013; 2013 |
| References_xml | – volume: 2014 start-page: 1 year: 2014 end-page: 13 ident: b0205 article-title: Optimizing the joint replenishment and delivery scheduling problem under fuzzy environment using inverse weight fuzzy nonlinear programming method publication-title: Abstract and Applied Analysis – volume: 32 year: 2014 ident: b0105 article-title: Understanding the impact of risks on performance in internal and outsourced information technology projects: The role of strategic importance publication-title: International Journal of Project Management – volume: 50 start-page: 159 year: 2003 end-page: 175 ident: b0210 article-title: Time series forecasting using a hybrid ARIMA and neural network model publication-title: Neurocomputing – volume: 29 start-page: 79 year: 2008 end-page: 88 ident: b0035 article-title: A fractionally integrated autoregressive moving average approach to forecasting tourism demand publication-title: Tourism Management – volume: 2013 start-page: 1 year: 2013 end-page: 10 ident: b0100 article-title: A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under E-supply chain environment publication-title: The Scientific World Journal – volume: 171 start-page: 674 year: 2006 end-page: 692 ident: b0130 article-title: Scheduling flow shops using differential evolution algorithm publication-title: European Journal of Operational Research – volume: 30 start-page: 219 year: 2000 end-page: 232 ident: b0195 article-title: A weight initialization method for improving training speed in feedforward neural network publication-title: Neurocomputing – volume: 37 start-page: 4049 year: 2009 end-page: 4054 ident: b0055 article-title: Energy demand estimation of South Korea using artificial neural network publication-title: Energy Policy – volume: 29 start-page: 429 year: 2012 end-page: 441 ident: b0170 article-title: A differential evolution algorithm for joint replenishment problem using direct grouping and its application publication-title: Expert Systems – volume: 45 start-page: 850 year: 2012 end-page: 858 ident: b0215 article-title: Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting publication-title: Energy – volume: 33 start-page: 61 year: 2010 end-page: 106 ident: b0125 article-title: Recent advances in differential evolution: A survey and experimental analysis publication-title: Artificial Intelligence Review – volume: 54 start-page: 207 year: 2013 end-page: 215 ident: b0140 article-title: Modeling and optimization for the joint replenishment and delivery problem with heterogeneous items publication-title: Knowledge-Based Systems – volume: 94 start-page: 251 year: 2012 end-page: 256 ident: b0095 article-title: Forecasting nonlinear time series of energy consumption using a hybrid dynamic model publication-title: Applied Energy – volume: 77 start-page: 29 year: 2007 end-page: 53 ident: b0010 article-title: Comparison of ARIMA, neural networks and hybrid models in time series: Tourist arrival forecasting publication-title: Journal of Statistical Computation and Simulation – volume: 11 start-page: 341 year: 1997 end-page: 359 ident: b0145 article-title: Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces publication-title: Journal of Global Optimization – volume: 29 start-page: 1 year: 2013 end-page: 8 ident: b0160 article-title: Optimized light guide plate optical brightness parameter: integrating back-propagation neural network (BPN) and revised genetic algorithm (GA) publication-title: Materials and Manufacturing – volume: 20 start-page: 134 year: 2002 end-page: 144 ident: b0045 article-title: Comparing predictive accuracy publication-title: Journal of Business & Economic Statistics – year: 1976 ident: b0020 article-title: Time series analysis: Forecasting and control – volume: 140 start-page: 411 year: 1977 end-page: 431 ident: b0025 article-title: A survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis publication-title: Journal of the Royal Statistical Society (Series A) – volume: 37 start-page: 4901 year: 2009 end-page: 4909 ident: b0190 article-title: A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand publication-title: Energy Policy – volume: 47 start-page: 115 year: 2009 end-page: 125 ident: b0115 article-title: Financial time series forecasting using independent component analysis and support vector regression publication-title: Decision Support Systems – volume: 14 start-page: 35 year: 1998 end-page: 62 ident: b0220 article-title: Forecasting with artificial neural networks: The state of the art publication-title: International Journal of Forecasting – volume: 160 start-page: 501 year: 2005 end-page: 514 ident: b0230 article-title: Neural network forecasting for seasonal and trend time series publication-title: European Journal of Operational Research – volume: 39 start-page: 4181 year: 2012 end-page: 4189 ident: b0165 article-title: Continuous review inventory models with a mixture of backorders and lost sales under fuzzy demand and different decision situations publication-title: Expert Systems with Applications – volume: 6 start-page: 954 year: 2013 end-page: 968 ident: b0085 article-title: Hybrid fuzzy auto-regressive integrated moving average (FARIMAH) model for forecasting the foreign exchange markets publication-title: International Journal of Computational Intelligence Systems – volume: 40 start-page: 5501 year: 2013 end-page: 5506 ident: b0150 article-title: A Bayesian regularized artificial neural network for stock market forecasting publication-title: Expert Systems with Applications – volume: 38 start-page: 9862 year: 2011 end-page: 9866 ident: b0065 article-title: Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir publication-title: Expert Systems with Applications – volume: 39 start-page: 4344 year: 2012 end-page: 4357 ident: b0075 article-title: A new class of hybrid models for time series forecasting publication-title: Expert Systems with Applications – volume: 41 start-page: 1792 year: 2014 end-page: 1805 ident: b0040 article-title: RFID technology investment evaluation model for the stochastic joint replenishment and delivery problem publication-title: Expert Systems with Applications – volume: 2013 start-page: 1 year: 2013 end-page: 17 ident: b9000 article-title: A new improv ed quantum evolution algorithm with local search procedure for capacitated vehicle routing problem publication-title: Mathematical Problems in Engineering – volume: 191 start-page: 2873 year: 2002 end-page: 2886 ident: b0235 article-title: An evaluation of back-propagation neural networks for the optimal design of structural systems: Part I. Training procedures publication-title: Computer Methods in Applied Mechanics and Engineering – volume: 2 start-page: 1980 year: 2004 end-page: 1987 ident: b0155 article-title: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems publication-title: Proceedings of IEEE Congress on Evolutionary Computation – volume: 185 start-page: 1026 year: 2007 end-page: 1037 ident: b0240 article-title: A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training publication-title: Applied Mathematics and Computation – volume: 2014 start-page: 1 year: 2014 end-page: 12 ident: b0180 article-title: Optimizing the joint replenishment and channel coordination problem under supply chain environment using a simple and effective differential evolution algorithm publication-title: Discrete Dynamics in Nature and Society – volume: 40 start-page: 1014 year: 2013 end-page: 1023 ident: b0015 article-title: ANN-based residential water end-use demand forecasting model publication-title: Expert Systems with Applications – volume: 46 start-page: 2669 year: 2005 end-page: 2688 ident: b0135 article-title: Support vector machines with simulated annealing algorithms in electricity load forecasting publication-title: Energy Conversion and Management – volume: 126 start-page: 202 year: 2014 end-page: 212 ident: b0030 article-title: The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process publication-title: Fuel – volume: 58 start-page: 283 year: 1999 end-page: 294 ident: b0090 article-title: Autoregressive integrated moving averages (ARIMA) modeling of a traffic noise time series publication-title: Applied Acoustics – volume: 3991 start-page: 728 year: 2006 end-page: 735 ident: b0185 article-title: The criticality of spare parts evaluating model using an artificial neural network approach publication-title: Lecture Notes in Computer Science – volume: 28 start-page: 372 year: 2006 end-page: 378 ident: b0060 article-title: The comparison of different feed forward neural network architectures for ECG signal diagnosis publication-title: Medical Engineering and Physics – volume: 28 start-page: 381 year: 2001 end-page: 396 ident: b0225 article-title: A simulation study of artificial neural networks for nonlinear time-series forecasting publication-title: Computers & Operations Research – volume: 2013 start-page: 1 year: 2013 end-page: 11 ident: b0175 article-title: An effective hybrid self-adapting differential evolution algorithm for the joint replenishment and location-inventory problem in a three-level supply chain publication-title: The Scientific World Journal – volume: 35 start-page: 1701 year: 2007 end-page: 1708 ident: b0050 article-title: ARIMA forecasting of primary energy demand by fuel in Turkey publication-title: Energy Policy – volume: 4 start-page: 358 year: 2013 end-page: 362 ident: b0200 article-title: Forecasting the number of human immunodeficiency virus infections in the Korean population using the autoregressive integrated moving average model publication-title: Osong Public Health and Research Perspectives – volume: 2 start-page: 1 year: 2014 end-page: 7 ident: b0005 article-title: Comparison of ARIMA and artificial neural networks models for stock price prediction publication-title: Journal of Applied Mathematics – volume: 37 start-page: 9643 year: 2013 end-page: 9651 ident: b0070 article-title: Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting publication-title: Applied Mathematical Modeling – volume: 31 start-page: 172 year: 2012 end-page: 188 ident: b0120 article-title: Forecasting performance of nonlinear models for intraday stock returns publication-title: Journal of Forecasting – volume: 72 start-page: 956 year: 2009 end-page: 967 ident: b0080 article-title: Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs) publication-title: Neurocomputing – volume: 52 start-page: 1148 year: 2014 end-page: 1173 ident: b0110 article-title: User liaisons’ perspective on behavior and outcome control in IT projects: Role of IT experience, behavior observability, and outcome measurability publication-title: Management Decision – volume: 38 start-page: 9862 issue: 8 year: 2011 ident: 10.1016/j.eswa.2014.08.018_b0065 article-title: Evolving neural network using real coded genetic algorithm for permeability estimation of the reservoir publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.02.046 – volume: 28 start-page: 381 issue: 4 year: 2001 ident: 10.1016/j.eswa.2014.08.018_b0225 article-title: A simulation study of artificial neural networks for nonlinear time-series forecasting publication-title: Computers & Operations Research doi: 10.1016/S0305-0548(99)00123-9 – volume: 35 start-page: 1701 issue: 3 year: 2007 ident: 10.1016/j.eswa.2014.08.018_b0050 article-title: ARIMA forecasting of primary energy demand by fuel in Turkey publication-title: Energy Policy doi: 10.1016/j.enpol.2006.05.009 – volume: 160 start-page: 501 issue: 2 year: 2005 ident: 10.1016/j.eswa.2014.08.018_b0230 article-title: Neural network forecasting for seasonal and trend time series publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2003.08.037 – volume: 2 start-page: 1 issue: 1 year: 2014 ident: 10.1016/j.eswa.2014.08.018_b0005 article-title: Comparison of ARIMA and artificial neural networks models for stock price prediction publication-title: Journal of Applied Mathematics doi: 10.1155/2014/614342 – volume: 52 start-page: 1148 issue: 6 year: 2014 ident: 10.1016/j.eswa.2014.08.018_b0110 article-title: User liaisons’ perspective on behavior and outcome control in IT projects: Role of IT experience, behavior observability, and outcome measurability publication-title: Management Decision doi: 10.1108/MD-08-2013-0430 – volume: 2013 start-page: 1 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b0175 article-title: An effective hybrid self-adapting differential evolution algorithm for the joint replenishment and location-inventory problem in a three-level supply chain publication-title: The Scientific World Journal doi: 10.1155/2013/270249 – volume: 2013 start-page: 1 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b0100 article-title: A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under E-supply chain environment publication-title: The Scientific World Journal doi: 10.1155/2013/125893 – volume: 191 start-page: 2873 issue: 25 year: 2002 ident: 10.1016/j.eswa.2014.08.018_b0235 article-title: An evaluation of back-propagation neural networks for the optimal design of structural systems: Part I. Training procedures publication-title: Computer Methods in Applied Mechanics and Engineering doi: 10.1016/S0045-7825(01)00372-3 – volume: 140 start-page: 411 issue: 4 year: 1977 ident: 10.1016/j.eswa.2014.08.018_b0025 article-title: A survey of statistical work on the Mackenzie River series of annual Canadian lynx trappings for the years 1821–1934 and a new analysis publication-title: Journal of the Royal Statistical Society (Series A) doi: 10.2307/2345277 – year: 1976 ident: 10.1016/j.eswa.2014.08.018_b0020 – volume: 32 year: 2014 ident: 10.1016/j.eswa.2014.08.018_b0105 article-title: Understanding the impact of risks on performance in internal and outsourced information technology projects: The role of strategic importance publication-title: International Journal of Project Management doi: 10.1016/j.ijproman.2014.01.012 – volume: 4 start-page: 358 issue: 6 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b0200 article-title: Forecasting the number of human immunodeficiency virus infections in the Korean population using the autoregressive integrated moving average model publication-title: Osong Public Health and Research Perspectives doi: 10.1016/j.phrp.2013.10.009 – volume: 126 start-page: 202 year: 2014 ident: 10.1016/j.eswa.2014.08.018_b0030 article-title: The genetic algorithm based back propagation neural network for MMP prediction in CO2-EOR process publication-title: Fuel doi: 10.1016/j.fuel.2014.02.034 – volume: 2013 start-page: 1 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b9000 article-title: A new improv ed quantum evolution algorithm with local search procedure for capacitated vehicle routing problem publication-title: Mathematical Problems in Engineering doi: 10.1155/2013/309750 – volume: 41 start-page: 1792 issue: 4 year: 2014 ident: 10.1016/j.eswa.2014.08.018_b0040 article-title: RFID technology investment evaluation model for the stochastic joint replenishment and delivery problem publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2013.08.078 – volume: 40 start-page: 1014 issue: 4 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b0015 article-title: ANN-based residential water end-use demand forecasting model publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2012.08.012 – volume: 50 start-page: 159 year: 2003 ident: 10.1016/j.eswa.2014.08.018_b0210 article-title: Time series forecasting using a hybrid ARIMA and neural network model publication-title: Neurocomputing doi: 10.1016/S0925-2312(01)00702-0 – volume: 14 start-page: 35 issue: 1 year: 1998 ident: 10.1016/j.eswa.2014.08.018_b0220 article-title: Forecasting with artificial neural networks: The state of the art publication-title: International Journal of Forecasting doi: 10.1016/S0169-2070(97)00044-7 – volume: 28 start-page: 372 issue: 4 year: 2006 ident: 10.1016/j.eswa.2014.08.018_b0060 article-title: The comparison of different feed forward neural network architectures for ECG signal diagnosis publication-title: Medical Engineering and Physics doi: 10.1016/j.medengphy.2005.06.006 – volume: 72 start-page: 956 issue: 4 year: 2009 ident: 10.1016/j.eswa.2014.08.018_b0080 article-title: Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs) publication-title: Neurocomputing doi: 10.1016/j.neucom.2008.04.017 – volume: 6 start-page: 954 issue: 5 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b0085 article-title: Hybrid fuzzy auto-regressive integrated moving average (FARIMAH) model for forecasting the foreign exchange markets publication-title: International Journal of Computational Intelligence Systems doi: 10.1080/18756891.2013.809937 – volume: 3991 start-page: 728 year: 2006 ident: 10.1016/j.eswa.2014.08.018_b0185 article-title: The criticality of spare parts evaluating model using an artificial neural network approach publication-title: Lecture Notes in Computer Science doi: 10.1007/11758501_97 – volume: 94 start-page: 251 year: 2012 ident: 10.1016/j.eswa.2014.08.018_b0095 article-title: Forecasting nonlinear time series of energy consumption using a hybrid dynamic model publication-title: Applied Energy doi: 10.1016/j.apenergy.2012.01.063 – volume: 31 start-page: 172 issue: 2 year: 2012 ident: 10.1016/j.eswa.2014.08.018_b0120 article-title: Forecasting performance of nonlinear models for intraday stock returns publication-title: Journal of Forecasting doi: 10.1002/for.1218 – volume: 29 start-page: 1 issue: 1 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b0160 article-title: Optimized light guide plate optical brightness parameter: integrating back-propagation neural network (BPN) and revised genetic algorithm (GA) publication-title: Materials and Manufacturing – volume: 171 start-page: 674 issue: 2 year: 2006 ident: 10.1016/j.eswa.2014.08.018_b0130 article-title: Scheduling flow shops using differential evolution algorithm publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2004.08.043 – volume: 45 start-page: 850 issue: 1 year: 2012 ident: 10.1016/j.eswa.2014.08.018_b0215 article-title: Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting publication-title: Energy doi: 10.1016/j.energy.2012.07.006 – volume: 40 start-page: 5501 issue: 14 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b0150 article-title: A Bayesian regularized artificial neural network for stock market forecasting publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2013.04.013 – volume: 2014 start-page: 1 year: 2014 ident: 10.1016/j.eswa.2014.08.018_b0205 article-title: Optimizing the joint replenishment and delivery scheduling problem under fuzzy environment using inverse weight fuzzy nonlinear programming method publication-title: Abstract and Applied Analysis – volume: 37 start-page: 4901 issue: 11 year: 2009 ident: 10.1016/j.eswa.2014.08.018_b0190 article-title: A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand publication-title: Energy Policy doi: 10.1016/j.enpol.2009.06.046 – volume: 20 start-page: 134 issue: 1 year: 2002 ident: 10.1016/j.eswa.2014.08.018_b0045 article-title: Comparing predictive accuracy publication-title: Journal of Business & Economic Statistics doi: 10.1198/073500102753410444 – volume: 2014 start-page: 1 year: 2014 ident: 10.1016/j.eswa.2014.08.018_b0180 article-title: Optimizing the joint replenishment and channel coordination problem under supply chain environment using a simple and effective differential evolution algorithm publication-title: Discrete Dynamics in Nature and Society doi: 10.1155/2014/675721 – volume: 37 start-page: 9643 issue: 23 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b0070 article-title: Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting publication-title: Applied Mathematical Modeling doi: 10.1016/j.apm.2013.05.016 – volume: 39 start-page: 4344 issue: 4 year: 2012 ident: 10.1016/j.eswa.2014.08.018_b0075 article-title: A new class of hybrid models for time series forecasting publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.09.157 – volume: 33 start-page: 61 issue: 1–2 year: 2010 ident: 10.1016/j.eswa.2014.08.018_b0125 article-title: Recent advances in differential evolution: A survey and experimental analysis publication-title: Artificial Intelligence Review doi: 10.1007/s10462-009-9137-2 – volume: 185 start-page: 1026 issue: 2 year: 2007 ident: 10.1016/j.eswa.2014.08.018_b0240 article-title: A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2006.07.025 – volume: 29 start-page: 79 issue: 1 year: 2008 ident: 10.1016/j.eswa.2014.08.018_b0035 article-title: A fractionally integrated autoregressive moving average approach to forecasting tourism demand publication-title: Tourism Management doi: 10.1016/j.tourman.2007.04.003 – volume: 30 start-page: 219 issue: 1 year: 2000 ident: 10.1016/j.eswa.2014.08.018_b0195 article-title: A weight initialization method for improving training speed in feedforward neural network publication-title: Neurocomputing doi: 10.1016/S0925-2312(99)00127-7 – volume: 2 start-page: 1980 year: 2004 ident: 10.1016/j.eswa.2014.08.018_b0155 article-title: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems publication-title: Proceedings of IEEE Congress on Evolutionary Computation – volume: 11 start-page: 341 issue: 4 year: 1997 ident: 10.1016/j.eswa.2014.08.018_b0145 article-title: Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces publication-title: Journal of Global Optimization doi: 10.1023/A:1008202821328 – volume: 29 start-page: 429 issue: 5 year: 2012 ident: 10.1016/j.eswa.2014.08.018_b0170 article-title: A differential evolution algorithm for joint replenishment problem using direct grouping and its application publication-title: Expert Systems doi: 10.1111/j.1468-0394.2011.00594.x – volume: 37 start-page: 4049 issue: 10 year: 2009 ident: 10.1016/j.eswa.2014.08.018_b0055 article-title: Energy demand estimation of South Korea using artificial neural network publication-title: Energy Policy doi: 10.1016/j.enpol.2009.04.049 – volume: 47 start-page: 115 issue: 2 year: 2009 ident: 10.1016/j.eswa.2014.08.018_b0115 article-title: Financial time series forecasting using independent component analysis and support vector regression publication-title: Decision Support Systems doi: 10.1016/j.dss.2009.02.001 – volume: 77 start-page: 29 issue: 1 year: 2007 ident: 10.1016/j.eswa.2014.08.018_b0010 article-title: Comparison of ARIMA, neural networks and hybrid models in time series: Tourist arrival forecasting publication-title: Journal of Statistical Computation and Simulation doi: 10.1080/10629360600564874 – volume: 46 start-page: 2669 issue: 17 year: 2005 ident: 10.1016/j.eswa.2014.08.018_b0135 article-title: Support vector machines with simulated annealing algorithms in electricity load forecasting publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2005.02.004 – volume: 54 start-page: 207 year: 2013 ident: 10.1016/j.eswa.2014.08.018_b0140 article-title: Modeling and optimization for the joint replenishment and delivery problem with heterogeneous items publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2013.09.013 – volume: 58 start-page: 283 issue: 3 year: 1999 ident: 10.1016/j.eswa.2014.08.018_b0090 article-title: Autoregressive integrated moving averages (ARIMA) modeling of a traffic noise time series publication-title: Applied Acoustics doi: 10.1016/S0003-682X(98)00078-4 – volume: 39 start-page: 4181 issue: 4 year: 2012 ident: 10.1016/j.eswa.2014.08.018_b0165 article-title: Continuous review inventory models with a mixture of backorders and lost sales under fuzzy demand and different decision situations publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.09.116 |
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| Snippet | •We propose a BPNN with adaptive differential evolution (ADE) for time series forecasting.•ADE is used to search for global initial connection weights and... The back propagation neural network (BPNN) can easily fall into the local minimum point in time series forecasting. A hybrid approach that combines the... |
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| SubjectTerms | Adaptive algorithms Artificial neural networks Back propagation Back propagation neural network Differential evolution algorithm Forecasting Mathematical models Neural networks Searching Thresholds Time series forecasting |
| Title | Back propagation neural network with adaptive differential evolution algorithm for time series forecasting |
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