Abstract
In this paper, a new approach for solving system of fully fuzzy nonlinear equations based on fuzzy neural network is presented. This method can also lead to improve numerical methods. In this work, an architecture of fuzzy neural networks is also proposed to find a fuzzy root of a system of fuzzy nonlinear equations (if exists) by introducing a learning algorithm. We propose a learning algorithm from the cost function for adjusting of fuzzy weights. Finally, we illustrate our approach by numerical examples.
Similar content being viewed by others
References
Abbasbandy S, Asady B (2004) Newton’s method for solving fuzzy nonlinear equations. Appl Math Comput 159:349–356
Abbasbandy S, Amirfakhrian M (2006) Numerical approximation of fuzzy functions by fuzzy polynomials. Appl Math Comput 174:1001–1006
Abbasbandy S, Ezzati R (2006) Newton’s method for solving a system of fuzzy nonlinear equations. Appl Math Comput 175:1189–1199
Abbasbandy S, Otadi M (2006) Numerical solution of fuzzy polynomials by fuzzy neural network. Appl Math Comput 181:1084–1089
Abbasbandy S, Otadi M, Mosleh M (2008) Numerical solution of a system of fuzzy polynomials by fuzzy neural network. Inform Sci 178:1948–1960
Alefeld G, Herzberger J (1983) Introduction to interval computations. Academic Press, New York
Amirfakhrian M (2008) Numerical solution of algebraic fuzzy equations with crisp variable by Gauss-Newton method. Appl Math Model 32:1859–1868
Dalalah DM (2009) Piecewise parametric polynomial fuzzy sets. Int J Approx Reason 50:1081–1096
Feuring TH, Lippe WM (1995) Fuzzy neural networks are universal approximators. IFSA world congress, Sao Paulo, vol. 2, pp 659–662
Hayashi Y, Buckley JJ, Czogala E (1993) Fuzzy neural network with fuzzy signals and weights. Internat J Intel Syst 8:527–537
Ishibuchi H, Nii M (2001) Numerical analysis of the learning of fuzzified neural networks from fuzzy if-then rules. Fuzzy Sets Syst 120:281–307
Ishibuchi H, Okada H, Tanaka H (1993) Fuzzy neural networks with fuzzy weights and fuzzy biases. Proceedings of the ICNN'93, San Francisco, pp 1650–1655
Li HX, Li LX, Wang JK (2003) Interpolation functions of feedforward neural networks. Comput Math Appl 46:1861–1874
Lin D, Wang X (2010) Observer-based decentralized fuzzy neural sliding mode control for interconnected unknown chaotic systems via network structure adaptation. Fuzzy Sets Syst 161:2066–2080
Lin D, Wang X (2011) Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters. Neurocomputing 74:2241–2249
Lin D, Wang X, Nian F, Zhang Y (2010) Dynamic fuzzy neural networks modeling and adaptive backstepping tracking control of uncertain chaotic systems. Neurocomputing 73:2873–2881
Ma M, Friedman M, Kandel A (1999) A new fuzzy arithmetic. Fuzzy Sets Syst 108:83–90
Mosleh M, Otadi M, Abbasbandy S (2010) Evaluation of fuzzy regression models by fuzzy neural network. J Comput Appl Math 234:825–834
Mosleh M, Otadi M, Abbasbandy S (2011) Evaluation of fuzzy polynomial regression model by fuzzy neural network. Appl Math Model 35:5400–5412
Otadi M, MOsleh M (2011) Simulation and evaluation of dual fully fuzzy linear systems by fuzzy neural network. Appl Math Model 35:5026–5039
Wang X, Zhao J (2010) Cryptanalysis on a parallel keyed hash function based on chaotic neural network. Neurocomputing 73:3224–3228
Zadeh LA (1975) The concept of a liguistic variable and its application to approximate reasoning: Parts 1-3. Inform Sci 8:199–249
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Otadi, M. System of fully fuzzy nonlinear equations with fuzzy neural network. Neural Comput & Applic 21 (Suppl 1), 369–376 (2012). https://doi.org/10.1007/s00521-012-0970-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-012-0970-x