Journal of Advances in Mathematics and Computer Science 34(1-2): 1-12, 2019; Article no.JAMCS.52081 ISSN: 2456-9968 (Past name: British Journal of Mathematics & Computer Science, Past ISSN: 2231-0851) _____________________________________ *Corresponding author: E-mail: suleyman.sengul@erdogan.edu.tr; Wong-Zakai Method Applications for Explicitly Solvable Stochastic Differential Equations Süleyman Şengül 1* and Mehmet Merdan 2 1 Department of Mathematics, Recep Tayyip Erdogan University, Rize, Turkey. 2 Department of Mathematical Engineering, Gumushane University, Gumushane, Turkey. Authors’ contributions: This work was carried out in collaboration between both authors. Author SS designed the study, performed the statistical analysis, wrote the protocol and wrote the first draft of the manuscript. Authors SS and MM managed the analyses of the study. Author MM managed the literature searches. Both authors read and approved the final manuscript. Article Information DOI: 10.9734/JAMCS/2019/v34i1-230202 Editor(s): (1) Dr. Dariusz Jacek Jakóbczak, Assistant Professor, Department of Computer Science and Electronics, Koszalin University of Technology, Poland. Reviewers: (1) Gabriel Obed Fosu, Presbyterian University College, Ghana. (2) Francisco Bulnes Iinamei, TESCHA, Mexico. (3) Upeksha Perera, University of Kelaniya, Sri Lanka. Complete Peer review History: https://sdiarticle4.com/review-history/52081 Received: 03 August 2019 Accepted: 16 October 2019 Published: 24 October 2019 _______________________________________________________________________________ Abstract In this study, three Ito stochastic differential equations with multiplicative noise are investigated with Wong-Zakai method. The stochastic differential equations are also analyzed by Euler-Maruyama, Milstein and Runge Kutta stochastic approximation methods. The relative errors of these three methods are compared and the performance of Wong-Zakai method is shown alongside numerical results. Keywords: Wong-Zakai method; stochastic differential equations; Euler method; Milstein method. 1 Introduction Mathematical models in the literature are mostly investigated by the use of deterministic differential equations and systems of equations. However it is known that most real life events contain components that act non-deterministically, i.e. randomly. The use of random and stochastic differential equation systems to Original Research Article