@inbook{8fcc3c5f94714fd39c4ab7eebbd65fa9,

title = "Neural network approach to solving fully fuzzy nonlinear systems",

abstract = "The value of fuzzy designs improves whenever a system cannot be validated in precise mathematical terminologies. In this book chapter, two types of neural networks are applied to obtain the approximate solutions of the fully fuzzy nonlinear system (FFNS). For obtaining the approximate solutions, a superior gradient descent algorithm is proposed in order to train the neural networks. Several examples are illustrated to disclose high precision as well as the effectiveness of the proposed methods. The MATLAB environment is utilized to generate the simulations.",

keywords = "fully fuzzy nonlinear system, neural network, approximate solution",

author = "Sina Razvarz and Raheleh Jafari and Alexander Gegov",

note = "Output does not have a DOI",

year = "2018",

month = may,

day = "1",

language = "English",

isbn = "9781536134148",

series = "Mathematics Research Developments",

publisher = "Nova Science Publishers",

pages = "46--68",

editor = "Terrell Harvey and Dallas Mullins",

booktitle = "Fuzzy Modelling and Control",

}