Ali Vahidian Kamyad
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ali Vahidian Kamyad has a total of 79 co-authors. Here are the top co-authors by number of shared publications:
- Sohrab Effati
Department of Applied Mathematics, Faculty of Mathematical science, Ferdowsi University of Mashhad, Mashhad, Iran
16 shared publications - Naser Pariz
Ferdowsi University of Mashhad
7 shared publications - Hamed Hashemi Mehne
Aerospace Research Institute
5 shared publications M. H. Farahi
Department of Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
4 shared publicationsM.H. Farahi
3 shared publications- Mahyar Taghizadeh Nouei
Department of Applied Mathematics, School of Mathematical Sciences Ferdowsi University of Mashhad, International Campus Mashhad Iran
3 shared publications
Latest Publications
- 2 March 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 11, 98-103
A proposed numerical solution of a class of fuzzy quadratic programming problems
In the real applied optimization problems, we usually face nonlinear fuzzy programming problems (FNLPPs). This paper focuses on a class of fuzzy quadratic programming problems (FQPPs) in which all of technical coefficients and constraint inequalities are fuzzy ones. Initially, some definitions of fuzzy mathematics are introduced and then, we discuss on fuzzy quadratic programming problems and introduce a new approach to find the approximate solution to these problems. Finally, we introduce some numerical examples to show the efficiency of the proposed approach.
- 2 March 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 11, 98-103
A proposed numerical solution of a class of fuzzy quadratic programming problems
In the real applied optimization problems, we usually face nonlinear fuzzy programming problems (FNLPPs). This paper focuses on a class of fuzzy quadratic programming problems (FQPPs) in which all of technical coefficients and constraint inequalities are fuzzy ones. Initially, some definitions of fuzzy mathematics are introduced and then, we discuss on fuzzy quadratic programming problems and introduce a new approach to find the approximate solution to these problems. Finally, we introduce some numerical examples to show the efficiency of the proposed approach.
- 2 February 2019
- Published by Springer Nature in Nonlinear Dynamics
- Vol. 96 (1) , 57-74
Optimal control of non-smooth fractional-order systems based on extended Caputo derivative
- 2 February 2019
- Published by Springer Nature in Nonlinear Dynamics
- Vol. 96 (1) , 57-74
Optimal control of non-smooth fractional-order systems based on extended Caputo derivative
- Open Access
- 1 January 2019
- Published by Inderscience Publishers in International Journal of Information and Decision Sciences
- Vol. 11 (1) , 22
Fuzzy classification as a decision making problem in hesitant environments
- 1 January 2019
- Published by Inderscience Publishers in International Journal of Information and Decision Sciences
- Vol. 11 (1) , 22
Open AccessFuzzy classification as a decision making problem in hesitant environments
- Open Access
- 1 October 2018
- Vol. 8 (2) , 1-24
Measurable functions approach for approximate solutions of Linear space-time-fractional diffusion problems
- 1 October 2018
- Vol. 8 (2) , 1-24
Open AccessMeasurable functions approach for approximate solutions of Linear space-time-fractional diffusion problems
- 27 February 2017
- Published by Springer Nature in Computational and Applied Mathematics
- Vol. 37 (S1) , 158-182
Approximation methods for solving fractional optimal control problems
- 27 February 2017
- Published by Springer Nature in Computational and Applied Mathematics
- Vol. 37 (S1) , 158-182
Approximation methods for solving fractional optimal control problems
- 4 January 2017
- Published by Springer Nature in SeMA Journal
- Vol. 74 (4) , 585-603
Numerical solutions for solving a class of fractional optimal control problems via fixed-point approach
- 4 January 2017
- Published by Springer Nature in SeMA Journal
- Vol. 74 (4) , 585-603
Numerical solutions for solving a class of fractional optimal control problems via fixed-point approach
- 25 January 2016
- Published by Wiley in Expert Systems
- Vol. 33 (3) , 230-238
Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro‐fuzzy inference system and K‐means clustering
In this paper, a fuzzy expert system based on adaptive neuro‐fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p‐value of less than 0.05 in chi‐square or t‐student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.
- 25 January 2016
- Published by Wiley in Expert Systems
- Vol. 33 (3) , 230-238
Fuzzy risk assessment of mortality after coronary surgery using combination of adaptive neuro‐fuzzy inference system and K‐means clustering
In this paper, a fuzzy expert system based on adaptive neuro‐fuzzy inference system (ANFIS) is introduced to assess the mortality after coronary bypass surgery. In preprocessing phase, the attributes were reduced using a univariant analysis in order to make the classifier system more effective. Prognostic factors with a p‐value of less than 0.05 in chi‐square or t‐student analysis were given to inputs ANFIS classifier. The correct diagnosis performance of the proposed fuzzy system was calculated in 824 samples. To demonstrate the usefulness of the proposed system, the study compared the performance of fuzzy system based on ANFIS method through the binary logistic regression with the same attributes. The experimental results showed that the fuzzy model (accuracy: 96.4%; sensitivity: 66.6%; specificity: 97.2%; and area under receiver operating characteristic curve: 0.82) consistently outperformed the logistic regression (accuracy: 89.4%; sensitivity: 47.6%; specificity: 89.4%; and area under receiver operating characteristic curve: 0.62). The obtained classification accuracy of fuzzy expert system was very promising with regard to the traditional statistical methods to predict mortality after coronary bypass surgery such as binary logistic regression model.
- 1 January 2016
- Published by Elsevier in Applied Mathematical Modelling
- Vol. 40 (2) , 886-903
Smoothing approach for a class of nonsmooth optimal control problems
- 1 January 2016
- Published by Elsevier in Applied Mathematical Modelling
- Vol. 40 (2) , 886-903
Smoothing approach for a class of nonsmooth optimal control problems
- 1 September 2015
- Published by Elsevier in Communications in Nonlinear Science and Numerical Simulation
- Vol. 26 (1-3) , 276-277
Corrigendum to “Modified fractional Euler method for solving Fuzzy Fractional Initial Value Problem” [Commun Nonlinear Sci Numer Simul 18 (2013) 12–21]
- 1 September 2015
- Published by Elsevier in Communications in Nonlinear Science and Numerical Simulation
- Vol. 26 (1-3) , 276-277
Corrigendum to “Modified fractional Euler method for solving Fuzzy Fractional Initial Value Problem” [Commun Nonlinear Sci Numer Simul 18 (2013) 12–21]
- 29 June 2015
- Published by SAGE Publications in Journal of Vibration and Control
- Vol. 22 (8) , 2120-2134
An efficient method to solve a fractional differential equation by using linear programming and its application to an optimal control problem
This paper presents a new idea to solve fractional differential equations and fractional optimal control problems. The fractional derivative is defined in the Grunwald–Letnikov sense. The method is based on the linear programming problem. In this paper, by using first the concept of fractional derivatives, we will suggest a method where an equation with a fractional derivative is changed to a linear programming problem, and by solving it the fractional derivative will be obtained. Actually this suggested method is based on the minimization of total error. Some numerical examples are provided to confirm the accuracy of the proposed method.
- 29 June 2015
- Published by SAGE Publications in Journal of Vibration and Control
- Vol. 22 (8) , 2120-2134
An efficient method to solve a fractional differential equation by using linear programming and its application to an optimal control problem
This paper presents a new idea to solve fractional differential equations and fractional optimal control problems. The fractional derivative is defined in the Grunwald–Letnikov sense. The method is based on the linear programming problem. In this paper, by using first the concept of fractional derivatives, we will suggest a method where an equation with a fractional derivative is changed to a linear programming problem, and by solving it the fractional derivative will be obtained. Actually this suggested method is based on the minimization of total error. Some numerical examples are provided to confirm the accuracy of the proposed method.