Accès libre

Methods of Fuzzy Set in Simulation for Predicting Unobserved States of the Ecological and Geoengineering Systems

À propos de cet article

Citez

1. Michna, J., Ekmanis, J., Zeltins, N., Zebergs, V., & Siemianowicz, J. (2012). Innovation Risk Management in the Rational Energy Use (Part 2). Latvian Journal of Physics and Technical Sciences, 49 (1), 3–15. doi: https://doi.org/10.2478/v10047-012-0001-910.2478/v10047-012-0001-9 Search in Google Scholar

2. Fayyad, U.M., Candel, A., Ario de la Rubia E., Pafka, S., Chong, A., Lee, J-Y. (2017). Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess? In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 31–32). doi: https://doi.org/10.1145/3097983.312099810.1145/3097983.3120998 Search in Google Scholar

3. Mika, M. (2017). Interoperability Cadastral Data in the System Approach. Journal of Ecological Engineering, 18 (2), 150–156. https://doi.org/10.12911/22998993/6830310.12911/22998993/68303 Search in Google Scholar

4. Dychko, A., Yeremeyev, I., Kyselov, V., Remez, N., & Kniazevych, A. (2019). Ensuring Reliability of Control Data in Engineering Systems. Latvian Journal of Physics and Technical Sciences, 56 (6), 57–69. doi: https://doi.org/10.2478/lpts-2019-003510.2478/lpts-2019-0035 Search in Google Scholar

5. Dychko, A., Remez, N., Kyselov, V., Kraychuk, S., Ostapchuk, N., Kniazevych, A. (2020). Monitoring and Biochemical Treatment of Wastewater. Journal of Ecological Engineering, 21 (4), 150–159. doi: https://doi.org/10.12911/22998993/11981110.12911/22998993/119811 Search in Google Scholar

6. Johnston, M., & Kazemzadeh, E. (2018). U.S. Patent No. 10,114,612. Washington, DC: U.S. Patent and Trademark Office. Search in Google Scholar

7. Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Elsevier. Search in Google Scholar

8. Zhu, W., Sun, W., & Romagnoli, J. (2018). Adaptive k-Nearest-Neighbor Method for Process Monitoring. Industrial & Engineering Chemistry Research, 57 (7), 2574–2586. doi: 10.1021/acs.iecr.7b0377110.1021/acs.iecr.7b03771 Search in Google Scholar

9. Diduk, N.N. (2014). The Measures of Internal and External Information (on Example of Probabilistic Situations of Uncertainty). Part IV. System Research and Information Technologies, 1, 113–129. Search in Google Scholar

10. Jurasz, J., Piasecki, A., & Kaźmierczak, B. (2019). Sewage Volume Forecasting on a Day-Ahead Basis – Analysis of Input Variables Uncertainty. Journal of Ecological Engineering, 20 (9), 70–79. doi: https://doi.org/10.12911/22998993/11250710.12911/22998993/112507 Search in Google Scholar

11. Pichler, M., Boreux, V., Klein, A., Schleuning, M., & Hartig F. (2019). Machine Learning Algorithms to Infer Trait-Matching and Predict Species Interactions in Ecological Networks. Methods in Ecology and Evolution, 11, 281–293. doi: 10.1111/2041-210X.1332910.1111/2041-210X.13329 Search in Google Scholar

12. Minhas, R., Kleer, J., Matei, I., Bhaskar, S., Janssen, B., Bobrow, D.G. & Kurtoglu, T. (2014). Using fault augmented modelica models for diagnostics. In Proceedings of the 10th International Modelica Conference 2014 (pp. 437–445), 10-12 March 2014, Lund, Sweden. Linkping University Electronic Press. doi: https://doi.org/10.3384/ecp1409643710.3384/ecp14096437 Search in Google Scholar

eISSN:
2255-8896
Langue:
Anglais
Périodicité:
6 fois par an
Sujets de la revue:
Physics, Technical and Applied Physics