Abstract
The aim of this study is to develop a fuzzy neural network-based support vector regression model (FNN-SVR) for mapping crisp-input and fuzzy-output variables. In this model, an artificial neural network (ANN) estimator based on multilayer perceptron (MLP) is considered as the kernel function of the SVR, whereas asymmetric triangular fuzzy H-level sets are assumed for model parameters including weight and biases of the ANN model. A genetic algorithm (GA) with real coding is implemented to optimize the model parameters during the training phase. To evaluate the efficiency and applicability of the proposed model, it is applied for simulating and regionalizing nitrate concentration in Karaj Aquifer in Iran. The goodness-of-fit criteria indicate a better performance of the FNN-SVR compared to some benchmark models such as geostatistic techniques as well as traditional SVR models with linear, quadratic, polynomial, and Gaussian kernel functions for modeling nitrate concentrations in groundwater.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.