A survey on applications of neuro-fuzzy models

Artificial intelligence techniques such as neuro-fuzzy have been successfully applied in a wide variety of uses to be mentioned as economics, engineering, social science, and business. In order to show the implementations of neuro-fuzzy in engineering the most recent researches in the area of neuro-fuzzy are covered in this paper. As many researchers have effectively utilized neuro-fuzzy in engineering applications, detailed studies are provided in this work for stimulating future researches

Solution of Fuzzy Differential Equations Using Fuzzy Sumudu Transforms

The uncertain nonlinear systems can be modeled with fuzzy differential equations (FDEs) and the solutions of these equations are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this paper, the solutions of FDEs are approximated by utilizing the fuzzy Sumudu transform (FST) method. Significant theorems are suggested in order to explain the properties of FST. The proposed method is validated with three real examples

Numerical Solution of Fuzzy Differential Equations with Z-numbers using Fuzzy Sumudu Transforms

The uncertain nonlinear systems can be modeled with fuzzy differential equations (FDEs) and the solutions of these equations are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this paper, the solutions of FDEs are approximated by utilizing the fuzzy Sumudu transform (FST) method. Here, the uncertainties are in the sense of Z-numbers. Important theorems are laid down to illustrate the properties of FST. The theoretical analysis and simulation results show that this new technique is effective to estimate the solutions of FDEs

Numerical Solution of Fuzzy Differential Equations with Z-numbers Using Bernstein Neural Networks

The uncertain nonlinear systems can be modeled with fuzzy equations or fuzzy differential equations (FDEs) by incorporating the fuzzy set theory. The solutions of them are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this paper, the solutions of FDEs are approximated by two types of Bernstein neural networks. Here, the uncertainties are in the sense of Z-numbers. Initially the FDE is transformed into four ordinary differential equations (ODEs) with Hukuhara differentiability. Then neural models are constructed with the structure of ODEs. With modified back propagation method for Z- number variables, the neural networks are trained. The theory analysis and simulation results show that these new models, Bernstein neural networks, are effective to estimate the solutions of FDEs based on Z-numbers