
-
Previous Article
A proximal ADMM with the Broyden family for convex optimization problems
- JIMO Home
- This Issue
-
Next Article
The joint location-transportation model based on grey bi-level programming for early post-earthquake relief
A goethite process modeling method by asynchronous fuzzy cognitive Network based on an improved constrained chicken swarm optimization algorithm
School of Automation, Central South University, Changsha 410083, China |
In order to solve the problem that the mechanism model of nonlinear system with uncertainty is difficult to establish, a modeling method of nonlinear system based on Asynchronous Fuzzy Cognitive Network (AFCN) is proposed. This method combines fuzzy cognitive network with time-lag system, and extends the node state values and weights of fuzzy cognitive network to the time interval, which enhances the adaptability of the model. At the same time an improved constrained chicken swarm optimization algorithm(ICCSOA) is proposed to identify model parameters of AFCN. A lag matrix corresponding to the actual measured values of the system lag of the nodes in the AFCN model is introduced, and a correction term including the difference between the measured values and the predicted values of the system is added to the model parameter updating mechanism. The simulation experiment results of goethite process system shows this modeling method can be used to model complex systems with uncertainties or partial missing data. The control model based on the established system model can make correct control decisions. ICCSOA has the advantages of fast convergence speed and accurate learning results, whose global search ability and convergence accuracy are higher than those of CSO algorithm, which can be widely used to the modeling of uncertain systems.
References:
[1] |
A. P. Antigoni and P. P. Groumpos, Modeling of parkinson's disease using fuzzy cognitive maps and non-linear hebbian learning, International Journal on Artificial Intelligence Tools, 23 (2014), 1450010. Google Scholar |
[2] |
N. Chen, J. Y. Dai, X. J. Zhou, Q. Q. Yang and W. H. Gui,
Distributed model predictive control of iron precipitation process by goethite based on dual iterative method, International Journal of Control Automation and Systems, 17 (2019), 1233-1245.
doi: 10.1007/s12555-017-0742-6. |
[3] |
N. Chen, J. Y. Dai, W. H. Gui, Y. Q. Guo and J. Q. Zhou,
A hybrid prediction model with a selectively updating strategy for iron removal process in zinc hydrometallurgy, Science China Information Sciences, 63 (2020), 119205.
doi: 10.1007/s11432-018-9711-2. |
[4] |
N. Chen, Y. Fan, W. H. Gui, C. H. Yang and Z. H. Jiang, Hybrid modeling and control of iron precipitation by goethite process, Chinese Journal of Nonferrous Metals, 24 (2014), 254-261. Google Scholar |
[5] |
B. Christen, C. Kjeldsen, T. Dalgaard and J. Martin-Ortega,
Can fuzzy cognitive mapping help in agricultural policy design and communication?, Land Use Policy, 45 (2015), 64-75.
doi: 10.1016/j.landusepol.2015.01.001. |
[6] |
N. Chen, J. Q. Zhou, J. J. Peng, W. H. Gui and J. Y. Dai,
Modeling of goethite iron precipitation process based on time-delay fuzzy gray cognitive network, Journal of Central South University, 26 (2019), 63-74.
doi: 10.1007/s11771-019-3982-1. |
[7] |
N. Chen, J. J. Peng, L. Wang, Y. Q. Guo and W. H. Gui, Fuzzy grey cognitive networks modeling and its application, Acta Automatica Sinica, 44 (2018), 1227-1236. Google Scholar |
[8] |
N. Chen, L. Wang, J. J. Peng, B. Liu and W. H. Gui, Improved nonlinear Hebbian learning algorithm based on fuzzy cognitive networks model, Control Theory and Applications, 33 (2016), 1273-1280. Google Scholar |
[9] |
Y. G. Deng, Q. Y. Chen, Z. L. Yin and P. M. Zhang, Iron removal from zine leaching solution by goethite method, Non-ferrous Metal, 62 (2014), 80-84. Google Scholar |
[10] |
Z. Djaafar, A. Yahia and N. Farid, Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems, Computers and Industrial Engineering, 129 (2019), 377-391. Google Scholar |
[11] |
S. Fatahi and H. Moradi,
A fuzzy cognitive map model to calculate a user's desirability based on personality in e-learning environments, Computers in Human Behavior, 63 (2016), 272-281.
doi: 10.1016/j.chb.2016.05.041. |
[12] |
B. Kosko,
Fuzzy cognitive maps, International Journal of Man-Machine Studie, 24 (1986), 65-75.
doi: 10.1016/S0020-7373(86)80040-2. |
[13] |
V. Kreinovich and C. D. Stylios,
Why fuzzy cognitive maps are efficient, International Journal of Computers Communications & Control, 10 (2015), 825-833.
doi: 10.15837/ijccc.2015.6.2073. |
[14] |
T. Kottas, D. Stimoniaris and D. Tsiamitros,
New operation scheme and control of Smart Grids using Fuzzy Cognitive Networks, PowerTech, 2015 IEEE Eindhoven, 63 (2015), 1-5.
doi: 10.1109/PTC.2015.7232563. |
[15] |
D. B. Li and J. M. Jiang, Present situation and development trend of zinc smelting technology at home and abroad, China Metal Bulletin, 6 (2015), 41-44. Google Scholar |
[16] |
P. C. Marchal, J. G. García and J. G. Ortega,
Application of fuzzy cognitive maps and run-to-run control to a decision support system for global set-point determination, IEEE Transactions on Systems Man & Cybernetics Systems, 47 (2017), 2256-2267.
doi: 10.1109/TSMC.2016.2646762. |
[17] |
A. Mourhir, E. I. Papageorgiou, K. Kokkinos and T. Rachidi, Exploring precision farming scenarios using Fuzzy Cognitive Maps, Sustainability, 9 7 (2017), 1241.
doi: 10.3390/su9071241. |
[18] |
X. B. Meng, Y. Liu and X. Z. Gao, A new bio-inspired algorism: Chicken swarm optimization, Proc of International Conference in Swarm of Intelligence, Cham: Springer, (2014), 86-94. Google Scholar |
[19] |
M. Obiedat and S. Samarasinghe,
A novel semi-quantitative Fuzzy Cognitive Map model for complex systems for addressing challenging participatory real life problems, Applied Soft Computing, 48 (2016), 91-110.
doi: 10.1016/j.asoc.2016.06.001. |
[20] |
E. I. Papageorgiou, K. D. Aggelopoulou and T. A. Gemtos,
Yield prediction in apples using Fuzzy Cognitive Map learning approach, Computers & Electronics in Agriculture, 91 (2013), 19-29.
doi: 10.1016/j.compag.2012.11.008. |
[21] |
K. E. Parsopoulos, E. I. Papagergiou, P. P. Groumpos and M. N. Vrahatis,
A first study of fuzzy cognitive maps learning using particle swarm optimization, Proceedings of IEEE Congress on Evolutionary Computation 2003, (2003), 1440-1447.
doi: 10.1109/CEC.2003.1299840. |
[22] |
J. Solana-Gutiérrez, G. Rincón, C. Alonso and D. García-de-Jalón, Using fuzzy cognitive maps for predicting river managementresponses: A case study of the Esla River basin, Spain, Ecological Modelling, 360 (2017), 260-269. Google Scholar |
[23] |
W. Stach, L. Kurgan, W. Pedrycz and M. Reformat,
Genetic learning of fuzzy cognitive maps, Fuzzy Sets and Systems, 153 (2005), 371-401.
doi: 10.1016/j.fss.2005.01.009. |
[24] |
D. H. Wu, S. P. Xu and F. Kong,
Convergence analysis and improvement of the chicken swarm optimization algorithm, IEEE Access, 4 (2019), 9400-9412.
doi: 10.1109/ACCESS.2016.2604738. |
[25] |
B. Wang, W. Li, X. H. Chen and H. H. Chen, Improved chicken swarm algorithms based on chaos theory and its application in wind power interval prediction, Mathematical Problems in Engineering, (2019), Art. ID 1240717, 10 pp.
doi: 10.1155/2019/1240717. |
[26] |
Z. Q. Wu, D. Q. Yu and X. H. Kang,
Application of improved chicken swarm optimization for MPPT in photovoltaic system, Optimal Control Applications and Method, 39 (2018), 1029-1042.
doi: 10.1002/oca.2394. |
[27] |
X. W. Yu, L. X. Zhou and X. Y. Li,
A novel hybrid localization scheme for deep mine based on wheel graph and chicken swarm optimization, Computer Networks, 154 (2019), 73-78.
doi: 10.1016/j.comnet.2019.02.011. |
[28] |
Y. L. Zhang, Modeling and Control of Dynamic System Based on Fuzzy Cognitive Maps, Dalian University of Technology, 2012. Google Scholar |
show all references
References:
[1] |
A. P. Antigoni and P. P. Groumpos, Modeling of parkinson's disease using fuzzy cognitive maps and non-linear hebbian learning, International Journal on Artificial Intelligence Tools, 23 (2014), 1450010. Google Scholar |
[2] |
N. Chen, J. Y. Dai, X. J. Zhou, Q. Q. Yang and W. H. Gui,
Distributed model predictive control of iron precipitation process by goethite based on dual iterative method, International Journal of Control Automation and Systems, 17 (2019), 1233-1245.
doi: 10.1007/s12555-017-0742-6. |
[3] |
N. Chen, J. Y. Dai, W. H. Gui, Y. Q. Guo and J. Q. Zhou,
A hybrid prediction model with a selectively updating strategy for iron removal process in zinc hydrometallurgy, Science China Information Sciences, 63 (2020), 119205.
doi: 10.1007/s11432-018-9711-2. |
[4] |
N. Chen, Y. Fan, W. H. Gui, C. H. Yang and Z. H. Jiang, Hybrid modeling and control of iron precipitation by goethite process, Chinese Journal of Nonferrous Metals, 24 (2014), 254-261. Google Scholar |
[5] |
B. Christen, C. Kjeldsen, T. Dalgaard and J. Martin-Ortega,
Can fuzzy cognitive mapping help in agricultural policy design and communication?, Land Use Policy, 45 (2015), 64-75.
doi: 10.1016/j.landusepol.2015.01.001. |
[6] |
N. Chen, J. Q. Zhou, J. J. Peng, W. H. Gui and J. Y. Dai,
Modeling of goethite iron precipitation process based on time-delay fuzzy gray cognitive network, Journal of Central South University, 26 (2019), 63-74.
doi: 10.1007/s11771-019-3982-1. |
[7] |
N. Chen, J. J. Peng, L. Wang, Y. Q. Guo and W. H. Gui, Fuzzy grey cognitive networks modeling and its application, Acta Automatica Sinica, 44 (2018), 1227-1236. Google Scholar |
[8] |
N. Chen, L. Wang, J. J. Peng, B. Liu and W. H. Gui, Improved nonlinear Hebbian learning algorithm based on fuzzy cognitive networks model, Control Theory and Applications, 33 (2016), 1273-1280. Google Scholar |
[9] |
Y. G. Deng, Q. Y. Chen, Z. L. Yin and P. M. Zhang, Iron removal from zine leaching solution by goethite method, Non-ferrous Metal, 62 (2014), 80-84. Google Scholar |
[10] |
Z. Djaafar, A. Yahia and N. Farid, Multi-objective chicken swarm optimization: A novel algorithm for solving multi-objective optimization problems, Computers and Industrial Engineering, 129 (2019), 377-391. Google Scholar |
[11] |
S. Fatahi and H. Moradi,
A fuzzy cognitive map model to calculate a user's desirability based on personality in e-learning environments, Computers in Human Behavior, 63 (2016), 272-281.
doi: 10.1016/j.chb.2016.05.041. |
[12] |
B. Kosko,
Fuzzy cognitive maps, International Journal of Man-Machine Studie, 24 (1986), 65-75.
doi: 10.1016/S0020-7373(86)80040-2. |
[13] |
V. Kreinovich and C. D. Stylios,
Why fuzzy cognitive maps are efficient, International Journal of Computers Communications & Control, 10 (2015), 825-833.
doi: 10.15837/ijccc.2015.6.2073. |
[14] |
T. Kottas, D. Stimoniaris and D. Tsiamitros,
New operation scheme and control of Smart Grids using Fuzzy Cognitive Networks, PowerTech, 2015 IEEE Eindhoven, 63 (2015), 1-5.
doi: 10.1109/PTC.2015.7232563. |
[15] |
D. B. Li and J. M. Jiang, Present situation and development trend of zinc smelting technology at home and abroad, China Metal Bulletin, 6 (2015), 41-44. Google Scholar |
[16] |
P. C. Marchal, J. G. García and J. G. Ortega,
Application of fuzzy cognitive maps and run-to-run control to a decision support system for global set-point determination, IEEE Transactions on Systems Man & Cybernetics Systems, 47 (2017), 2256-2267.
doi: 10.1109/TSMC.2016.2646762. |
[17] |
A. Mourhir, E. I. Papageorgiou, K. Kokkinos and T. Rachidi, Exploring precision farming scenarios using Fuzzy Cognitive Maps, Sustainability, 9 7 (2017), 1241.
doi: 10.3390/su9071241. |
[18] |
X. B. Meng, Y. Liu and X. Z. Gao, A new bio-inspired algorism: Chicken swarm optimization, Proc of International Conference in Swarm of Intelligence, Cham: Springer, (2014), 86-94. Google Scholar |
[19] |
M. Obiedat and S. Samarasinghe,
A novel semi-quantitative Fuzzy Cognitive Map model for complex systems for addressing challenging participatory real life problems, Applied Soft Computing, 48 (2016), 91-110.
doi: 10.1016/j.asoc.2016.06.001. |
[20] |
E. I. Papageorgiou, K. D. Aggelopoulou and T. A. Gemtos,
Yield prediction in apples using Fuzzy Cognitive Map learning approach, Computers & Electronics in Agriculture, 91 (2013), 19-29.
doi: 10.1016/j.compag.2012.11.008. |
[21] |
K. E. Parsopoulos, E. I. Papagergiou, P. P. Groumpos and M. N. Vrahatis,
A first study of fuzzy cognitive maps learning using particle swarm optimization, Proceedings of IEEE Congress on Evolutionary Computation 2003, (2003), 1440-1447.
doi: 10.1109/CEC.2003.1299840. |
[22] |
J. Solana-Gutiérrez, G. Rincón, C. Alonso and D. García-de-Jalón, Using fuzzy cognitive maps for predicting river managementresponses: A case study of the Esla River basin, Spain, Ecological Modelling, 360 (2017), 260-269. Google Scholar |
[23] |
W. Stach, L. Kurgan, W. Pedrycz and M. Reformat,
Genetic learning of fuzzy cognitive maps, Fuzzy Sets and Systems, 153 (2005), 371-401.
doi: 10.1016/j.fss.2005.01.009. |
[24] |
D. H. Wu, S. P. Xu and F. Kong,
Convergence analysis and improvement of the chicken swarm optimization algorithm, IEEE Access, 4 (2019), 9400-9412.
doi: 10.1109/ACCESS.2016.2604738. |
[25] |
B. Wang, W. Li, X. H. Chen and H. H. Chen, Improved chicken swarm algorithms based on chaos theory and its application in wind power interval prediction, Mathematical Problems in Engineering, (2019), Art. ID 1240717, 10 pp.
doi: 10.1155/2019/1240717. |
[26] |
Z. Q. Wu, D. Q. Yu and X. H. Kang,
Application of improved chicken swarm optimization for MPPT in photovoltaic system, Optimal Control Applications and Method, 39 (2018), 1029-1042.
doi: 10.1002/oca.2394. |
[27] |
X. W. Yu, L. X. Zhou and X. Y. Li,
A novel hybrid localization scheme for deep mine based on wheel graph and chicken swarm optimization, Computer Networks, 154 (2019), 73-78.
doi: 10.1016/j.comnet.2019.02.011. |
[28] |
Y. L. Zhang, Modeling and Control of Dynamic System Based on Fuzzy Cognitive Maps, Dalian University of Technology, 2012. Google Scholar |






Test Function | Expression | Symbol | Range of values | Value of optimal solution |
Sphere | F1 | [-100, 100] | 0 | |
Rosenbrock | F2 | [-30, 30] | 0 | |
High Conditioned Elliptic | F3 | [-100, 100] | 0 | |
Bent Cigar | F4 | [-100, 100] | 0 | |
Discus | F5 | [-100, 100] | 0 | |
Rotated hyper-ellipsoid | F6 | [-100, 100] | 0 | |
Rotated rastrigin | F7 | [-100, 100] | 0 |
Test Function | Expression | Symbol | Range of values | Value of optimal solution |
Sphere | F1 | [-100, 100] | 0 | |
Rosenbrock | F2 | [-30, 30] | 0 | |
High Conditioned Elliptic | F3 | [-100, 100] | 0 | |
Bent Cigar | F4 | [-100, 100] | 0 | |
Discus | F5 | [-100, 100] | 0 | |
Rotated hyper-ellipsoid | F6 | [-100, 100] | 0 | |
Rotated rastrigin | F7 | [-100, 100] | 0 |
Title Symbol | Algorithms | Optimal value | Worst value | Average value | Standard deviation | Stable step |
F1 | CSO | 1.8488e-133 | 2.8082e-123 | 6.0362e-125 | 3.9681e-124 | 30 |
ICSO | 7.0233e-133 | 6.4356e-125 | 3.1959e-126 | 1.1357e-125 | 26 | |
ICCSO | 6.9244e-182 | 2.0424e-163 | 5.0084e-165 | 3.2075e-164 | 24 | |
F2 | CSO | 6.1449 | 7.97 | 6.9651 | 0.3031 | 33 |
ICSO | 5.9715 | 7.2163 | 6.7664 | 0.3324 | 29 | |
ICCSO | 2.1398e-07 | 4.9115e-05 | 6.6865e-06 | 8.2512e-06 | 27 | |
F3 | CSO | 6.7415e-127 | 2.1961e-117 | 7.328e-119 | 3.3125e-118 | 32 |
ICSO | 9.006e-128 | 1.4093e-118 | 3.3872e-120 | 1.9906e-119 | 28 | |
ICCSO | 8.9815e-177 | 1.2078e-160 | 2.4737e-162 | 1.7073e-161 | 25 | |
F4 | CSO | 3.1473e-127 | 1.0419e-117 | 2.9859e-119 | 1.4796e-118 | 36 |
ICSO | 9.4786e-127 | 5.5614e-119 | 2.5308e-120 | 8.8336e-120 | 28 | |
ICCSO | 5.148e-178 | 5.2831e-161 | 1.1419e-162 | 7.372e-162 | 25 | |
F5 | CSO | 1.9478e-131 | 9.9848e-123 | 5.2391e-124 | 1.7265-123 | 35 |
ICSO | 9.8894e-132 | 2.2241e-123 | 4.6208e-125 | 3.1431e-124 | 27 | |
ICCSO | 4.9026e-181 | 1.0179e-166 | 5.8305e-168 | 2.5381e-167 | 23 | |
F6 | CSO | 5.4415e-127 | 1.5173e-109 | 6.328e-129 | 3.3225e-117 | 37 |
ICSO | 8.016e-138 | 1.9214e-140 | 4.6672e-110 | 2.1066e-119 | 31 | |
ICCSO | 9.148e-165 | 1.3078e-187 | 2.6728e-154 | 1.7953e-151 | 28 | |
F7 | CSO | 4.1923e-172 | 1.0419e-117 | 2.1659e-139 | 1.4707e-118 | 36 |
ICSO | 9.4554e-125 | 4.6634e-122 | 2.5325e-113 | 7.7543e-122 | 32 | |
ICCSO | 6.1579e-148 | 5.2635e-177 | 1.9719e-172 | 6.3823e-165 | 27 |
Title Symbol | Algorithms | Optimal value | Worst value | Average value | Standard deviation | Stable step |
F1 | CSO | 1.8488e-133 | 2.8082e-123 | 6.0362e-125 | 3.9681e-124 | 30 |
ICSO | 7.0233e-133 | 6.4356e-125 | 3.1959e-126 | 1.1357e-125 | 26 | |
ICCSO | 6.9244e-182 | 2.0424e-163 | 5.0084e-165 | 3.2075e-164 | 24 | |
F2 | CSO | 6.1449 | 7.97 | 6.9651 | 0.3031 | 33 |
ICSO | 5.9715 | 7.2163 | 6.7664 | 0.3324 | 29 | |
ICCSO | 2.1398e-07 | 4.9115e-05 | 6.6865e-06 | 8.2512e-06 | 27 | |
F3 | CSO | 6.7415e-127 | 2.1961e-117 | 7.328e-119 | 3.3125e-118 | 32 |
ICSO | 9.006e-128 | 1.4093e-118 | 3.3872e-120 | 1.9906e-119 | 28 | |
ICCSO | 8.9815e-177 | 1.2078e-160 | 2.4737e-162 | 1.7073e-161 | 25 | |
F4 | CSO | 3.1473e-127 | 1.0419e-117 | 2.9859e-119 | 1.4796e-118 | 36 |
ICSO | 9.4786e-127 | 5.5614e-119 | 2.5308e-120 | 8.8336e-120 | 28 | |
ICCSO | 5.148e-178 | 5.2831e-161 | 1.1419e-162 | 7.372e-162 | 25 | |
F5 | CSO | 1.9478e-131 | 9.9848e-123 | 5.2391e-124 | 1.7265-123 | 35 |
ICSO | 9.8894e-132 | 2.2241e-123 | 4.6208e-125 | 3.1431e-124 | 27 | |
ICCSO | 4.9026e-181 | 1.0179e-166 | 5.8305e-168 | 2.5381e-167 | 23 | |
F6 | CSO | 5.4415e-127 | 1.5173e-109 | 6.328e-129 | 3.3225e-117 | 37 |
ICSO | 8.016e-138 | 1.9214e-140 | 4.6672e-110 | 2.1066e-119 | 31 | |
ICCSO | 9.148e-165 | 1.3078e-187 | 2.6728e-154 | 1.7953e-151 | 28 | |
F7 | CSO | 4.1923e-172 | 1.0419e-117 | 2.1659e-139 | 1.4707e-118 | 36 |
ICSO | 9.4554e-125 | 4.6634e-122 | 2.5325e-113 | 7.7543e-122 | 32 | |
ICCSO | 6.1579e-148 | 5.2635e-177 | 1.9719e-172 | 6.3823e-165 | 27 |
Algorithm | ICCSO | GA | PSO |
0.3600 | 0.3600 | 0.3600 | |
0.3559 | 0.3591 | 0.3490 | |
Steady step | 9 | 10 | 13 |
RMSE | 0.0017 | 0.0022 | 0.0019 |
MAE | 0.0011 | 0.0019 | 0.0015 |
MAX | 0.0026 | 0.0028 | 0.0029 |
SD | 0.0009 | 0.0017 | 0.0020 |
Algorithm | ICCSO | GA | PSO |
0.3600 | 0.3600 | 0.3600 | |
0.3559 | 0.3591 | 0.3490 | |
Steady step | 9 | 10 | 13 |
RMSE | 0.0017 | 0.0022 | 0.0019 |
MAE | 0.0011 | 0.0019 | 0.0015 |
MAX | 0.0026 | 0.0028 | 0.0029 |
SD | 0.0009 | 0.0017 | 0.0020 |
Working conditions | RMSE | MAE | MAX | SD | ||
SSS | 0.1400 | 0.1421 | 0.0013 | 0.0011 | 0.0023 | 0.0011 |
SSB | 0.1500 | 0.1490 | ||||
SBS | 0.1518 | 0.1533 | ||||
SBB | 0.1177 | 0.1199 | ||||
MSS | 0.2900 | 0.2906 | ||||
MSB | 0.2694 | 0.2689 | ||||
MBS | 0.3659 | 0.3659 | ||||
MBB | 0.0100 | 0.0120 | ||||
BSS | 0.1620 | 0.1627 | ||||
BSB | 0.1997 | 0.2002 | ||||
BBS | 0.3400 | 0.3407 | ||||
BBB | 0.3200 | 0.3189 |
Working conditions | RMSE | MAE | MAX | SD | ||
SSS | 0.1400 | 0.1421 | 0.0013 | 0.0011 | 0.0023 | 0.0011 |
SSB | 0.1500 | 0.1490 | ||||
SBS | 0.1518 | 0.1533 | ||||
SBB | 0.1177 | 0.1199 | ||||
MSS | 0.2900 | 0.2906 | ||||
MSB | 0.2694 | 0.2689 | ||||
MBS | 0.3659 | 0.3659 | ||||
MBB | 0.0100 | 0.0120 | ||||
BSS | 0.1620 | 0.1627 | ||||
BSB | 0.1997 | 0.2002 | ||||
BBS | 0.3400 | 0.3407 | ||||
BBB | 0.3200 | 0.3189 |
[1] |
Mohammed Abdulrazaq Kahya, Suhaib Abduljabbar Altamir, Zakariya Yahya Algamal. Improving whale optimization algorithm for feature selection with a time-varying transfer function. Numerical Algebra, Control & Optimization, 2021, 11 (1) : 87-98. doi: 10.3934/naco.2020017 |
[2] |
Claudia Lederman, Noemi Wolanski. An optimization problem with volume constraint for an inhomogeneous operator with nonstandard growth. Discrete & Continuous Dynamical Systems - A, 2020 doi: 10.3934/dcds.2020391 |
[3] |
Wolfgang Riedl, Robert Baier, Matthias Gerdts. Optimization-based subdivision algorithm for reachable sets. Journal of Computational Dynamics, 2021, 8 (1) : 99-130. doi: 10.3934/jcd.2021005 |
[4] |
Vo Van Au, Mokhtar Kirane, Nguyen Huy Tuan. On a terminal value problem for a system of parabolic equations with nonlinear-nonlocal diffusion terms. Discrete & Continuous Dynamical Systems - B, 2021, 26 (3) : 1579-1613. doi: 10.3934/dcdsb.2020174 |
[5] |
Editorial Office. Retraction: Honggang Yu, An efficient face recognition algorithm using the improved convolutional neural network. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 901-901. doi: 10.3934/dcdss.2019060 |
[6] |
Mahdi Karimi, Seyed Jafar Sadjadi. Optimization of a Multi-Item Inventory model for deteriorating items with capacity constraint using dynamic programming. Journal of Industrial & Management Optimization, 2020 doi: 10.3934/jimo.2021013 |
[7] |
Guo Zhou, Yongquan Zhou, Ruxin Zhao. Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem. Journal of Industrial & Management Optimization, 2021, 17 (2) : 533-548. doi: 10.3934/jimo.2019122 |
[8] |
Cheng Peng, Zhaohui Tang, Weihua Gui, Qing Chen, Jing He. A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size. Journal of Industrial & Management Optimization, 2021, 17 (1) : 205-220. doi: 10.3934/jimo.2019107 |
[9] |
Hedy Attouch, Aïcha Balhag, Zaki Chbani, Hassan Riahi. Fast convex optimization via inertial dynamics combining viscous and Hessian-driven damping with time rescaling. Evolution Equations & Control Theory, 2021 doi: 10.3934/eect.2021010 |
[10] |
Xiaoping Zhai, Yongsheng Li. Global large solutions and optimal time-decay estimates to the Korteweg system. Discrete & Continuous Dynamical Systems - A, 2021, 41 (3) : 1387-1413. doi: 10.3934/dcds.2020322 |
[11] |
Maoli Chen, Xiao Wang, Yicheng Liu. Collision-free flocking for a time-delay system. Discrete & Continuous Dynamical Systems - B, 2021, 26 (2) : 1223-1241. doi: 10.3934/dcdsb.2020251 |
[12] |
Ting Liu, Guo-Bao Zhang. Global stability of traveling waves for a spatially discrete diffusion system with time delay. Electronic Research Archive, , () : -. doi: 10.3934/era.2021003 |
[13] |
Reza Chaharpashlou, Abdon Atangana, Reza Saadati. On the fuzzy stability results for fractional stochastic Volterra integral equation. Discrete & Continuous Dynamical Systems - S, 2020 doi: 10.3934/dcdss.2020432 |
[14] |
Youshan Tao, Michael Winkler. Critical mass for infinite-time blow-up in a haptotaxis system with nonlinear zero-order interaction. Discrete & Continuous Dynamical Systems - A, 2021, 41 (1) : 439-454. doi: 10.3934/dcds.2020216 |
[15] |
Jianquan Li, Xin Xie, Dian Zhang, Jia Li, Xiaolin Lin. Qualitative analysis of a simple tumor-immune system with time delay of tumor action. Discrete & Continuous Dynamical Systems - B, 2020 doi: 10.3934/dcdsb.2020341 |
[16] |
Jie Zhang, Yuping Duan, Yue Lu, Michael K. Ng, Huibin Chang. Bilinear constraint based ADMM for mixed Poisson-Gaussian noise removal. Inverse Problems & Imaging, , () : -. doi: 10.3934/ipi.2020071 |
[17] |
Qiang Fu, Yanlong Zhang, Yushu Zhu, Ting Li. Network centralities, demographic disparities, and voluntary participation. Mathematical Foundations of Computing, 2020, 3 (4) : 249-262. doi: 10.3934/mfc.2020011 |
[18] |
Ali Mahmoodirad, Harish Garg, Sadegh Niroomand. Solving fuzzy linear fractional set covering problem by a goal programming based solution approach. Journal of Industrial & Management Optimization, 2020 doi: 10.3934/jimo.2020162 |
[19] |
Shipra Singh, Aviv Gibali, Xiaolong Qin. Cooperation in traffic network problems via evolutionary split variational inequalities. Journal of Industrial & Management Optimization, 2020 doi: 10.3934/jimo.2020170 |
[20] |
Yicheng Liu, Yipeng Chen, Jun Wu, Xiao Wang. Periodic consensus in network systems with general distributed processing delays. Networks & Heterogeneous Media, 2020 doi: 10.3934/nhm.2021002 |
2019 Impact Factor: 1.366
Tools
Metrics
Other articles
by authors
[Back to Top]