Neo-Fuzzy-Neuron Network with Genetic ProgrammingLearning for Non Linear Regression Problems
Abstract
This paper proposes two new approaches to create the rule-base for the Neo-Fuzzy-Neuron Network. The approaches use Genetic Programming (GP) to generate the rules associated with each input, creating and adjusting the membership functions. A Gradient based-method is used to update parameters. The evaluation of the models is performed considering forecast and non-linear system identification problems. The results obtained are compared with alternative models of the state of the art. The results show that the proposed algorithms are efficient and competitive.
Keywords:
Neo-Fuzzy-Neuron, Genetic Programming, Neuro-Fuzzy Network
References
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Lemos, A., Caminhas, W., and Gomide, F. (2011). Fuzzy evolving linear regression trees. Evolving Systems, 2(1):1–14.
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Shihabudheen, K. and Pillai, G. (2018). Recent advances in neuro-fuzzy system. Know.Based Syst., 152(C):136–162.
Silva, A. M., Caminhas, W., Lemos, A., and Gomide, F. (2014). A fast learning algorithm for evolving neo-fuzzy neuron. Appl. Soft Comput., 14:194–209.
Smith, S. F. (1980). A Learning System Based on Genetic Adaptive Algorithms. PhD thesis, Pittsburgh, PA, USA. AAI8112638.
Soliman, M. A., Hasanien, H. M., Azazi, H. Z., El-kholy, E. E., and Mahmoud, S. A. (2018). Hybrid anfis-ga-based control scheme for performance enhancement of a gridconnected wind generator. IET Renewable Power Generation, 12(7):832–843.
Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC15(1):116–132.
Yamakawa, T., Uchino, E., Miki, T., and Kusabagi, H. (1992). A neo fuzzy neuron and its applications to system identification and predictions to system behavior. In Proceedings of the International Conference on Fuzzy Logic and Neural Networks, pages 477–484. IEEE.
Zaychenko, Y. and Gasanov, A. (2012). Investigations of cascade neo-fuzzy neural networks in the problem of forecasting at the stock exchange. In 2012 IV International Conference ”Problems of Cybernetics and Informatics”(PCI), pages 1–3.
Abdollahzade, M., Miranian, A., Hassani, H., and Iranmanesh, H. (2015). A new hybrid enhanced local linear neuro-fuzzy model based on the optimized singular spectrum analysis and its application for nonlinear and chaotic time series forecasting. Inf. Sci., 295(C):107–125.
Badnjevic, A., Cifrek, M., Koruga, D., and Osmankovic, D. (2015). Neuro-fuzzy classification of asthma and chronic obstructive pulmonary disease. BMC Medical Informatics and Decision Making, 15(3):S1.
Bodyanskiy, Y., Kokshenev, I., and Kolodyazhniy, V. (2003). An adaptive learning algorithm for a neo fuzzy neuron. In EUSFLAT Conf., pages 375–379. Citeseer.
Cervantes, J., Yu, W., Salazar, S., and Chairez, I. (2017). Takagi–sugeno dynamic neurofuzzy controller of uncertain nonlinear systems. IEEE Transactions on Fuzzy Systems, 25(6):1601–1615.
Elhag, S., Fernández, A., Bawakid, A., Alshomrani, S., and Herrera, F. (2015). On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems. Expert Syst. Appl., 42(1):193–202.
Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems, 13(2):87–129. cite arxiv:cs/0102027Comment: 22 pages, 17 figures.
Herrera, F. (2008). Genetic fuzzy systems: taxonomy, current research trends and prospects. Evolutionary Intelligence, 1(1):27–46.
Holland, J. H. and Reitman, J. S. (1977). Cognitive systems based on adaptive algorithms. Number 63, pages 49–49. ACM, New York, NY, USA.
Kaur, A., Bhardwaj, A., and Been, U. A. H. (2014). Genetic neuro fuzzy system for hypertension diagnosis. International Journal of Computer Science and Information Technologies, 5(4):4986–4989.
Koshiyama, A. S., Vellasco, M. M., and Tanscheit, R. (2015). Gpfis-class: A genetic fuzzy system based on genetic programming for classification problems. Appl. Soft Comput., 37(C):561–571.
Kreinovich, V., Quintana, C., and Reznik, L. (1992). Gaussian membership functions are most adequate in representing uncertainty in measurements. In Proceedings of NAFIPS, pages 15–17.
KV, S. and Pillai, G. (2017). Regularized extreme learning adaptive neuro-fuzzy algorithm for regression and classification. Know.-Based Syst., 127(C):100–113.
Lemos, A., Caminhas, W., and Gomide, F. (2011). Fuzzy evolving linear regression trees. Evolving Systems, 2(1):1–14.
Mousavi, S., Esfahanipour, A., and Zarandi, M. H. F. (2015). Mgp-intactsky: Multitree genetic programming-based learning of interpretable and accurate tsk systems for dynamic portfolio trading. Applied Soft Computing, 34:449 – 462.
Pedrycz, W. (1991). Neurocomputations in relational systems. IEEE Trans. Pattern Anal. Mach. Intell., 13(3):289–297.
Poli, R., Langdon, W. B., and McPhee, N. F. (2008). A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd.
Shihabudheen, K. and Pillai, G. (2018). Recent advances in neuro-fuzzy system. Know.Based Syst., 152(C):136–162.
Silva, A. M., Caminhas, W., Lemos, A., and Gomide, F. (2014). A fast learning algorithm for evolving neo-fuzzy neuron. Appl. Soft Comput., 14:194–209.
Smith, S. F. (1980). A Learning System Based on Genetic Adaptive Algorithms. PhD thesis, Pittsburgh, PA, USA. AAI8112638.
Soliman, M. A., Hasanien, H. M., Azazi, H. Z., El-kholy, E. E., and Mahmoud, S. A. (2018). Hybrid anfis-ga-based control scheme for performance enhancement of a gridconnected wind generator. IET Renewable Power Generation, 12(7):832–843.
Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC15(1):116–132.
Yamakawa, T., Uchino, E., Miki, T., and Kusabagi, H. (1992). A neo fuzzy neuron and its applications to system identification and predictions to system behavior. In Proceedings of the International Conference on Fuzzy Logic and Neural Networks, pages 477–484. IEEE.
Zaychenko, Y. and Gasanov, A. (2012). Investigations of cascade neo-fuzzy neural networks in the problem of forecasting at the stock exchange. In 2012 IV International Conference ”Problems of Cybernetics and Informatics”(PCI), pages 1–3.
Published
2019-10-15
How to Cite
BRÁS, Glender; SILVA, Alisson.
Neo-Fuzzy-Neuron Network with Genetic ProgrammingLearning for Non Linear Regression Problems. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 154-165.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9280.
