The Research of Solving Inverse Problems of Complex Differential Equations

Kangshun Li, Yan Chen, Jun He

Research output: Chapter in Book/Report/Conference proceedingChapter


There are various complicated or non-linear system varying with the time (dynamic system) in the reality and scientific aspects, and such dynamic system is usually expressed by the differential equations. In this paper, a new GEP-based algorithm is put forward to solve the inverse problems of ordinary differential equation (ODE) and complex high-order differential equations by taking advantage of the self-adaptability, self-organization and self-study of Gene Expression Programming (GEP), which is difficult to solve by use traditional methods. Experiments show that this improved GEP algorithm can be used to solve the optimization problems of ordinary differential equations and complex differential equations in shorter time and with higher precision comparing with the traditional ones
Original languageEnglish
Title of host publicationBio-inspired Computing - Theories and Applications
Subtitle of host publication11th International Conference, BIC-TA 2016, Xi'an, China, October 28-30, 2016
EditorsMaoguo Gong, Linqiang Pen, Tao Song, Gexiang Zhang
PublisherSpringer Nature
Number of pages6
EditionPart 11
ISBN (Electronic)978-981-10-3614-9
ISBN (Print)978-981-10-3613-2
Publication statusPublished - 08 Jan 2017
Event11th International Conference, BIC-TA 2016 - Xi'an, China
Duration: 28 Oct 201630 Oct 2016

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer Singapore


Conference11th International Conference, BIC-TA 2016
Period28 Oct 201630 Oct 2016


  • gene expression programming
  • system of ordinary differential equations
  • inverse problem
  • Runge-Kutta algorithm


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