The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Design of Ensemble Neural Networks for Predicting the US Dollar/MX Time Series with Particle Swarm Optimization
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“most of the studies compared between ensemble classifiers [12, 18-20, 22, 23, 28] or regressors [21,30] machine learning algorithms, but not both.”
From A comprehensive evaluation of ensemble learning for stock-market prediction (2020) · cites this paper as [28]
Citations
Cites
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
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
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
Introduction to Time Series and Forecasting
Brockwell, P. J., & Davis, R. A. (2002) · Springer texts in statistics · doi:10.1007/b97391 · Cited by 3,975
Fundamentals of Computational Swarm Intelligence
Engelbrecht, A. P. (2005) · Medical Entomology and Zoology · Cited by 1,871
Type-2 Fuzzy Logic: Theory and Applications
Castillo, O., & Melin, P. (2008) · Studies in Fuzziness and Soft Computing · doi:10.1007/978-3-540-76284-3 · Cited by 594
Self-citationIntroduction to type-2 fuzzy logic systems
Karnik, N. N., & Mendel, J. M. (2002) · Proceedings of ... IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzzy.1998.686240 · Cited by 561
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
SHARKEY, A. J. C. (1999) · Cited by 482
Fuzzy sets and applications : selected papers
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Introductory Time Series with R
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Research works printed but not linked: 2
- Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice Hall, Englewood Cliffs (1996)
- Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings Intelligent Symposium, pp. 80–87 (2003)
Cited by
A systematic review of fundamental and technical analysis of stock market predictions
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09754-z · Cited by 547
A comprehensive evaluation of ensemble learning for stock-market prediction
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00299-5 · Cited by 329
Metadata
- Authors
- Pulido, M., Melin, P., & Castillo, O.
- Affiliations
- Instituto Tecnológico de Tijuana (MX)
- Corresponding author
- Pulido, Martha
- Venue
- Studies in Fuzziness and Soft Computing
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 317-329
- ISSN
- 1434-9922 (ISSN-L)
- 1860-0808
- OpenAlex
- W2487567064
- Cited by
- 4
- References
- 24
- Citations by year
- 2020: 2 · 2019: 1 · 2017: 1
- FWCI
- 2.09
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Studies in fuzziness and soft computing (publishedVersion)
- Funders
- Consejo Nacional de Ciencia y Tecnología
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- neural network ensembles, particle swarm optimization, time series forecasting, US dollar exchange rate, Mexican peso, type-1 fuzzy logic, type-2 fuzzy logic, hybrid neural-fuzzy systems, response integration
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Design of Ensemble Neural Networks for Predicting the US Dollar/MX Time Series with Particle Swarm Optimization. https://app.cochat.ai/papers/10.1007/978-3-319-32229-2_23
- The paper
- Pulido, M., Melin, P., & Castillo, O. (2016). Design of Ensemble Neural Networks for Predicting the US Dollar/MX Time Series with Particle Swarm Optimization. Studies in Fuzziness and Soft Computing. https://doi.org/10.1007/978-3-319-32229-2_23
Data sources
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.
