The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training
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Cites
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
A modified particle swarm optimizer
Shi, Y., & Eberhart, R. (2002) · 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) · doi:10.1109/icec.1998.699146 · Cited by 10,229
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,081
A discrete binary version of the particle swarm algorithm
Kennedy, J., & Eberhart, R. C. (2002) · 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation · doi:10.1109/icsmc.1997.637339 · Cited by 4,767
Empirical study of particle swarm optimization
Shi, Y., & Eberhart, R. C. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.785511 · Cited by 3,945
Parameter selection in particle swarm optimization
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Comparing inertia weights and constriction factors in particle swarm optimization
Eberhart, R. C., & Shi, Y. (2002) · Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512) · doi:10.1109/cec.2000.870279 · Cited by 2,966
Cited by
Feature selection in machine learning: A new perspective
Cai, J., Luo, J., Wang, S., & Yang, S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.11.077 · Cited by 2,061
Gao, S., Zhou, M., Wang, Y., Cheng, J., Yachi, H., & Wang, J. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2846646 · Cited by 698
Wang, L., Zeng, Y., & Chen, T. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.08.018 · Cited by 631
Metaheuristic design of feedforward neural networks: A review of two decades of research
Ojha, V. K., Abraham, A., & Snášel, V. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.01.013 · Cited by 606
On recent advances in PV output power forecast
Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.073 · Cited by 544
Metadata
- Authors
- Zhang, J.-R., Zhang, J., Lok, T.-M., & Lyu, M. R.
- Venue
- Applied Mathematics and Computation
- Publisher
- Elsevier BV
- Published
- 2007-02
- Volume
- 185
- Issue
- 2
- Pages
- 1026-1037
- ISSN
- 0096-3003 (ISSN-L)
- OpenAlex
- W2051680981
- Cited by
- 706
- References
- 22
- FWCI
- 17.22
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, J.-R., Zhang, J., Lok, T.-M., & Lyu, M. R. (2007). A hybrid particle swarm optimization–back-propagation algorithm for feedforward neural network training. Applied Mathematics and Computation. https://doi.org/10.1016/j.amc.2006.07.025
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
