Browsing by Author "Jafari, Raheleh"

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Citation Count: Jafarian, Ahmad...et al. (2016). "A novel computational approach to approximate fuzzy interpolation polynomials", Springerplus, Vol. 5.
A novel computational approach to approximate fuzzy interpolation polynomials
(Springer International Publishing AG, 2016) Jafarian, Ahmad; Jafari, Raheleh; Al Qurashi, Maysaa Mohamed; Baleanu, Dumitru; 56389
This paper build a structure of fuzzy neural network, which is well sufficient to gain a fuzzy interpolation polynomial of the form y(p) = a(n)x(p)(n) +... + a(1)x(p) + a(0) where a(j) is crisp number (for j = 0,..., n), which interpolates the fuzzy data (x(j), y(j)) (for j = 0,..., n). Thus, a gradient descent algorithm is constructed to train the neural network in such a way that the unknown coefficients of fuzzy polynomial are estimated by the neural network. The numeral experimentations portray that the present interpolation methodology is reliable and efficient.
Citation Count: Jafarian, A...et al. (2015). Solving fully fuzzy polynomials using feed-back neural networks. International Journal Of Computer Mathematics, 92(4), 742-755. http://dx.doi.org/10.1080/00207160.2014.907404
Solving fully fuzzy polynomials using feed-back neural networks
(Taylor&Francis LTD, 2015) Jafarian, Ahmad; Jafari, Raheleh; Golmankhaneh, Alireza K.; Baleanu, Dumitru
Recently, there has been a considerable amount of interest and practice in solving many problems of several applied fields by fuzzy polynomials. In this paper, we have designed an artificial fuzzified feed-back neural network. With this design, we are able to find a solution of fully fuzzy polynomial with degree n. This neural network can get a fuzzy vector as an input, and calculates its corresponding fuzzy output. It is clear that the input–output relation for each unit of fuzzy neural network is defined by the extension principle of Zadeh. In this work, a cost function is also defined for the level sets of fuzzy output and fuzzy target. Next a learning algorithm based on the gradient descent method will be defined that can adjust the fuzzy connection weights. Finally, our approach is illustrated by computer simulations on numerical examples. It is worthwhile to mention that application of this method in fluid mechanics has been shown by an example