Solving of stochastic Voltaire integral equations by fuzzy artificial neural network method
Subject Areas :
Statistics
Hadi Abtahi
1
,
Hamid Reza Rahimi
2
,
Maryam mosleh
3
1 - Department of Mathematics, Faculty of Science, Central Tehran Branch, Islamic Azad University, Tehran, IranDepartment of Mathematics, Faculty of Science, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Mathematics, Faculty of Science, Central Tehran
Branch, Islamic Azad University, Tehran, Iran
3 - Department of Mathematics, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
Received: 2020-09-08
Accepted : 2020-10-06
Published : 2021-09-23
Keywords:
معادلات انتگرال ولترای تصادفی. معادله انتگرال,
شبکه عصبی مصنوعی فازی,
Abstract :
Voltaire integral equations as the output of problems in basic sciences and engineering have a special application in advancing the solution of complex problems. One of the most widely used types, which consists of a random process under external motion, is the equations of random Volta integral. Solving this type of equation has always been a challenge for researchers. On the other hand, with the development of artificial intelligence and the presentation of fuzzy artificial neural network method as a model inspired by the process of thinking and analysis in the human brain, advanced models of algorithms have been designed. Some of these learning algorithms have been used in fuzzy artificial neural networks to solve equations. In this paper, using this method and designing a learning algorithm, the random equations of random Voltaire type is investigated. The method presented in this article, in addition to being more accurate than the previous methods, posseses more speed for solving problem. This topic provides an acceptable level of confidence for researchers when dealing with such issues.
References:
McCulloch, W.S. and W. Pitts, A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 1943. 5(4): p. 115-133
Subramanian, R., Emergent AI, Social Robots and the Law: Security, Privacy and Policy Issues. Subramanian, Ramesh (2017)" Emergent AI, Social Robots and the Law: Security, Privacy and Policy Issues," Journal of International, Technology and Information Management, 2017. 26(3).
Moor, J., The Dartmouth College artificial intelligence conference: The next fifty years. Ai Magazine, 2006. 27(4): p. 87-87.
McCarthy, J., Programs with common sense. 1960: RLE and MIT computation center.
Bryson, A.E. and Y.-C. Ho, Optimization, estimation and control. Ginn and Company, 1969.
Wan, E.A. Time series prediction by using a connectionist network with internal delay lines. in SANTA FE INSTITUTE STUDIES IN THE SCIENCES OF COMPLEXITY-PROCEEDINGS VOLUME-. 1993. Addison-Wesley publishing co.
Keller, J.M. and D.J. Hunt, Incorporating fuzzy membership functions into the perceptron algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985(6): p. 693-699.
Khan, E. and P. Venkatapuram. Neufuz: Neural network based fuzzy logic design algorithms. in [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems. 1993. IEEE.
Babakhani, A., E. Enteghami, and H. Hosseinzade, Numerical solution of Voltra algebraic integral equations by Taylor expansion method. Journal of New Researches in Mathematics, 2020. 6(24): p. 53-64
Adabitabar Firozja, M. and B. Agheli, A simple algorithm for solving the Volterra integral equation featuring a weakly singular kernel. Journal of New Researches in Mathematics, 2017. 2(8): p. 29-36.
فریبرزی عراقی, فرزانه جوان, عباسبندی, سعید. Solving Fuzzy Integral Equations of the Second Kind by using the Reproducing Kernel Hilbert Space Method. Journal of New Researches in Mathematics. 2020 Aug 27.
Adabitabar Firozja, M. and Agheli, B. A simple algorithm for solving the Volterra integral equation featuring a weakly singular kernel. Journal of New Researches in Mathematics. 2017
Mosleh, M. and M. Otadi, Simulation and evaluation of fuzzy differential equations by fuzzy neural network. Applied Soft Computing, 2012. 12(9): p. 2817-2827.
Leondes, C.T., Fuzzy logic and expert systems applications. Vol. 6. 1998: Elsevier
Balagurusamy, E., Computer Oriented Statistical and Numerical Methods. 1988: Macmillan India Limited.