HYBRID MODULAR NEURAL NETWORKS
https://doi.org/10.21686/2500-3925-2016-4-8-11
Abstract
About the Author
Alexey N. AverkinRussian Federation
PhD, associated professor of the Academic Department of Informatics;
chief researcher of intelligent department systems
References
1. Wang, J.-S.; Ning, C.-X. ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm. Information 2015, 6, 300–313.
2. Alizadeh M. et al. Forecasting Exchange Rates: A Neuro-Fuzzy Approach // IFSA/EUSFLAT Conf. – 2009. – С. 1745–1750.
3. Gunasekaran, M. and Ramaswami, K.S., A Fusion Model Integrating ANFIS and Artificial Immune Algorithm for Forecasting Indian Stock Market (June 22, 2011). Journal of Applied Sciences, 11(16): pp. 3028–3033.
4. Tokunaga et al., 2009] Tokunaga K., Furukawa T. Modular network SOM – Neural Networks. №22, 2009. Pp 82–90.
5. Koskela T. Neural network methods in analyzing and modelling time varying processes – Espoo, 2003. Pp. 1–72.
6. Lotfi A., Garibaldi J. In Applications and Science in Soft Computing, Advances in Soft Computing Series Springer, 2003. Pp. 3–8.
7. A. Averkin, V. Albu, Veaceslav, S. Ulyanov and others. Dynamic object identification with SOM-based neural networks. In: Computer Science Journal of Moldova, 2014, nr. 22 1/64, pp. 110–126.
Review
For citations:
Averkin A.N. HYBRID MODULAR NEURAL NETWORKS. Statistics and Economics. 2016;(4):8-11. (In Russ.) https://doi.org/10.21686/2500-3925-2016-4-8-11