Browsing by Author "Jafarian, Ahmad"

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Citation Count: Eskandari, Leila...et al. (2019). "A Modified and Enhanced Ant Colony Optimization Algorithm for Traveling Salesman Problem", MATHEMATICAL METHODS IN ENGINEERING: THEORETICAL ASPECTS, Vol. 23, pp. 257-265.
A Modified and Enhanced Ant Colony Optimization Algorithm for Traveling Salesman Problem
(2019) Eskandari, Leila; Jafarian, Ahmad; Rahimloo, Parastoo; Baleanu, Dumitru; 56389
Citation Count: Farnad, Behnam; Jafarian, Ahmad; Baleanu, Dumitru, "A new hybrid algorithm for continuous optimization problem", Applied Mathematical Modelling, Vol. 55, pp. 652-673, (2018)
A New Hybrid Algorithm for Continuous Optimization Problem
(Elsevier Science INC, 2018) Farnad, Behnam; Jafarian, Ahmad; Baleanu, Dumitru; 56389
This paper applies a new hybrid method by a combination of three population base algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Symbiotic Organisms Search (SOS). The proposed method has been inspired from natural selection process and it completes this process in GA by using the PSO and SOS. It tends to minimize the execution time and in addition to reduce the complexity. Symbiotic organisms search is a robust and powerful metaheuristic algorithm which has attracted increasing attention in recent decades. There are three alternative phases in the proposed algorithm: GA, which develops and selects best population for the next phases, PSO, which gets experiences for each appropriate solution and updates them as well and SOS, which benefits from previous phases and performs symbiotic interaction update phases in the real-world population. The proposed algorithm was tested on the set of best known unimodal and multimodal benchmark functions in various dimensions. It has further been evaluated in, the experiment on the clustering of benchmark datasets. The obtained results from basic and non-parametric statistical tests confirmed that this hybrid method dominates in terms of convergence, execution time, success rate. It optimizes the high dimensional and complex functions Rosenbrock and Griewank up to 10(-330) accuracy in less than 3 s, outperforming other known algorithms. It had also applied clustering datasets with minimum intra-cluster distance and error rate. (C) 2017 Elsevier Inc. All rights reserved.
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. (2014). "A Numerical Solution of the Urysohn-Type Fredholm İntegral Equations", Romanian Journal of Physics, Vol. 59, No. 7-8, pp. 625-635.
A Numerical Solution of the Urysohn-Type Fredholm İntegral Equations
(Editura Academiei Romane, 2014) Jafarian, Ahmad; Measoomy, S. A.; Golmankhaneh, Alireza K.; Baleanu, Dumitru; 56389
In the present paper, a combination of the Bernstein polynomials and artificial neural networks (ANNs) is presented for solving the non-linear Urysohn equation. These polynomials are utilized to reduce the solution of the given problem to the solution of a system of non-linear algebraic equations. The remaining set of nonlinear equations is solved numerically by using the ANNs approach to yield truncated Bernstein series coefficients of the solution function. Several illustrative examples with numerical simulations are provided to support the theoretical claims.
Citation Count: Jafarian, A...et al. (2014). "Analytical Approximate Solutions of the Zakharov-Kuznetsov Equations",Romanian Reports in Physics, Vol. 66, No. 2.
Analytical Approximate Solutions of the Zakharov-Kuznetsov Equations
(Editura Academiei Romane, 2014) Jafarian, Ahmad; Ghaderi, Pariya; Golmankhaneh, Alireza K.; Baleanu, Dumitru; 56389
In this paper, analytical approximate solutions for the Zakharov-Kuznetsov equations by homotopy analysis method (HAM) and the He's polynomials iterative method (HPIM) are presented. Our results indicate the remarkable efficiency of HAM as compared to HPIM. The convergence of these two methods is also analyzed.
Citation Count: Jafarian, A...et al. (2014). "Analytical Treatment of System of Abel Integral Equations By Homotopy Analysis Method",Romanian Reports in Physics, Vol. 66, No. 3, pp. 603-611.
Analytical Treatment of System of Abel Integral Equations By Homotopy Analysis Method
(Editura Academiei Romane, 2014) Jafarian, Ahmad; Ghaderi, Pariya; Golmankhaneh, Alireza K.; Baleanu, Dumitru; 56389
Abel equation has important applications in describing the least time for an object which is sliding on surface without friction in uniform gravity, and the classical theory of elasticity of materials is modeled by a system of Abel integral equations. In this manuscript, the homotopy analysis method is presented for obtaining analytical solutions of a system of Abel integral equations as fractional equations. The applied method has lessened the size of calculation and improved the accuracy of solution in the case of the singular Abel integral equation. The illustrated examples and numerical results have proved the assertion.
Citation Count: Jafarian, Ahmad; Baleanu, Dumitru (2017). Application of ANNs approach for wave-like and heat-like equations, Open Physics, 15(1), 1086-1094.
Application of ANNs approach for wave-like and heat-like equations
(De Gruyter Poland SP Zoo, 2017) Jafarian, Ahmad; Baleanu, Dumitru
Artificial neural networks are data processing systems which originate from human brain tissue studies. The remarkable abilities of these networks help us to derive desired results from complicated raw data. In this study, we intend to duplicate an efficient iterative method to the numerical solution of two famous partial differential equations, namely the wave-like and heat-like problems. It should be noted that many physical phenomena such as coupling currents in a flat multi-strand two-layer super conducting cable, non-homogeneous elastic waves in soils and earthquake stresses, are described by initial-boundary value wave and heat partial differential equations with variable coefficients. To the numerical solution of these equations, a combination of the power series method and artificial neural networks approach, is used to seek an appropriate bivariate polynomial solution of the mentioned initial-boundary value problem. Finally, several computer simulations confirmed the theoretical results and demonstrating applicability of the method.
Citation Count: Darwish, H...et al. (2015). "Applications of Artificial Neural Network Technique To Polypyrrole Gas Sensor Data for Environmental Analysis",Journal of Computational and Theoretical Nanoscience, Vol. 12, No. 11, pp. 4392-4398.
Applications of Artificial Neural Network Technique To Polypyrrole Gas Sensor Data for Environmental Analysis
(American Scientific Publishers, 2015) Darwish, Hamida; Jafarian, Ahmad; Baleanu, Dumitru; Senel, Mehmet; Okur, Salih; 56389
In this study, the electrochemical deposition technique was used to fabricate Polyprrole thin film. The QCM piezoelectric sensors have been used to investigate the possible sensing mechanisms and adsorption-desorption kinetics of the polyprrole films to compare sensor sensitivities of the atmosferic gasses such as humidity, CO2 and O2. The Langmuir model and ANN Technique have been used to Polypyrrole Gas Sensor Data for environmental analysis. For this, feedback, three layer ANN has been used for the experimental data for adsorption and desorption process of PPY versus humidity, PPY versus CO2 and PPy versus O2. Different number of hidden layer used in this work and good result gets with 14 neurons. Totally 2064 experimental data used for fitting ANN. The randomly selected data was used to training and the ANN was terminated when the error was less than 10-3.
Citation Count: Jafarian, Ahma; Mokhtarpour, Masoumeh; Baleanu, Dumitru, "Artificial neural network approach for a class of fractional ordinary differential equation", Neural Computing&Applications, Vol.28, No.4, pp.765-773, (2017).
Artificial neural network approach for a class of fractional ordinary differential equation
(Springer, 2017) Jafarian, Ahmad; Mokhtarpour, Masoumeh; Baleanu, Dumitru; 56389
The essential characteristic of artificial neural networks which against the logistic traditional systems is a data-based approach and has led a number of higher education scholars to investigate its efficacy, during the past few decades. The aim of this paper was concerned with the application of neural networks to approximate series solutions of a class of initial value ordinary differential equations of fractional orders, over a bounded domain. The proposed technique uses a suitable truncated power series of the solution function and transforms the original differential equation in a minimization problem. Then, the minimization problem is solved using an accurate neural network model to compute the parameters with high accuracy. Numerical results are given to validate the iterative method.
Citation Count: Jafarian, Ahmad...et al. (2014). "Homotopy analysis method for solving coupled Ramani equations", Romanian Journal of Physics, Vol. 59, No. 1-2, pp. 26-35.
Homotopy analysis method for solving coupled Ramani equations
(2014) Jafarian, Ahmad; Ghaderi, P.; Golmankhaneh, Alireza Khalili; Baleanu, Dumitru; 56389
In this manuscript, coupled Ramani equations are solved by means of an analytic technique, namely the homotopy analysis method (HAM). The HAM is a capable and a straightforward analytic tool for solving nonlinear problems and does not need small parameters in the governing equations and boundary/initial conditions. The result of this study presents the utility and sufficiency of HAM method. Comparisons demonstrate that there is a good agreement between the HAM solutions and the exact solutions.
Citation Count: Jafarian, Ahmad...et al. (2013). "Numerical solution of linear integral equations system using the Bernstein collocation method", Advances In Difference Equations.
Numerical Solution of Linear Integral Equations System Using the Bernstein Collocation Method
(Springer International Publishing AG, 2013) Jafarian, Ahmad; Nia, Safa Measoomy; Golmankhaneh, Alireza K.; Baleanu, Dumitru; 56389
Since in some application mathematical problems finding the analytical solution is too complicated, in recent years a lot of attention has been devoted by researchers to find the numerical solution of this equations. In this paper, an application of the Bernstein polynomials expansion method is applied to solve linear second kind Fredholm and Volterra integral equations systems. This work reduces the integral equations system to a linear system in generalized case such that the solution of the resulting system yields the unknown Bernstein coefficients of the solutions. Illustrative examples are provided to demonstrate the preciseness and effectiveness of the proposed technique. The results are compared with the exact solution by using computer simulations.
Citation Count: Jafarian, Ahmad...et al. (2018). "On artificial neural networks approach with new cost functions", Applied Mathematics and Computation, Vol. 339, pp. 546-555.
On artificial neural networks approach with new cost functions
(Elsevier Science INC, 2018) Jafarian, Ahmad; Nia, Safa Measoomy; Golmankhaneh, Alireza K.; Baleanu, Dumitru; 56389
In this manuscript, the artificial neural networks approach involving generalized sigmoid function as a cost function, and three-layered feed-forward architecture is considered as an iterative scheme for solving linear fractional order ordinary differential equations. The supervised back-propagation type learning algorithm based on the gradient descent method, is able to approximate this a problem on a given arbitrary interval to any desired degree of accuracy. To be more precise, some test problems are also given with the comparison to the simulation and numerical results given by another usual method. (C) 2018 Elsevier Inc. All rights reserved.
On Fuzzy Fractional Laplace Transformation
(Hindawi LTD, 2014) Jafarian, Ahmad; Golmankhaneh, Alireza K.; Baleanu, Dumitru; 56389
Fuzzy and fractional differential equations are used to model problems with uncertainty and memory. Using the fractional fuzzy Laplace transformation we have solved the fuzzy fractional eigenvalue differential equation. By illustrative examples we have shown the results.
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
Citation Count: Jafarian, Ahmad...et al. (2017). Using ANNs Approach for Solving Fractional Order Volterra Integro-differential Equations, International Journal Of Computational Intelligence Systems, 10(1), 470-480.
Using ANNs Approach for Solving Fractional Order Volterra Integro-differential Equations
(Atlantis Press, 2017) Jafarian, Ahmad; Rostami, Fariba; Golmankhaneh, Alireza K.; Baleanu, Dumitru; 56389
Indeed, interesting properties of artificial neural networks approach made this non-parametric model a powerful tool in solving various complicated mathematical problems. The current research attempts to produce an approximate polynomial solution for special type of fractional order Volterra integrodifferential equations. The present technique combines the neural networks approach with the power series method to introduce an efficient iterative technique. To do this, a multi-layer feed-forward neural architecture is depicted for constructing a power series of arbitrary degree. Combining the initial conditions with the resulted series gives us a suitable trial solution. Substituting this solution instead of the unknown function and employing the least mean square rule, converts the origin problem to an approximated unconstrained optimization problem. Subsequently, the resulting nonlinear minimization problem is solved iteratively using the neural networks approach. For this aim, a suitable three-layer feed-forward neural architecture is formed and trained using a back-propagation supervised learning algorithm which is based on the gradient descent rule. In other words, discretizing the differential domain with a classical rule produces some training rules. By importing these to designed architecture as input signals, the indicated learning algorithm can minimize the defined criterion function to achieve the solution series coefficients. Ultimately, the analysis is accompanied by two numerical examples to illustrate the ability of the method. Also, some comparisons are made between the present iterative approach and another traditional technique. The obtained results reveal that our method is very effective, and in these examples leads to the better approximations.