Simulating Nonlinear Waves and Partial Differential Equations via CNN—Part II: Typical Examples

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Tibor Kozek - , University of California at Berkeley (Author)
  • Tamas Roska - , University of California at Berkeley (Author)
  • Kåroly Lotz - , University of California at Berkeley (Author)
  • Leon O. Chua - , University of California at Berkeley (Author)
  • Dietrich Wolf - , University Hospital Frankfurt (Author)
  • Ronald Tetzlaff - , University Hospital Frankfurt (Author)
  • Frank Puffer - , University Hospital Frankfurt (Author)

Abstract

Application of cellular neural network (CNN) paradigm of locally connected analog array-computing structures is considered for solving partial differential equations (PDE’s) and systems of ordinary differential equations (ODE). Three examples are presented: a chain of particles with nonlinear interactions, solitons in a nonlinear Klein-Gordon equation, and an application of a reaction-diffusion CNN for fingerprint enhancement.

Details

Original languageEnglish
Pages (from-to)816-820
Number of pages5
JournalIEEE Transactions on Circuits and Systems : 1, Fundamental Theory and Applications
Volume42
Issue number10
Publication statusPublished - Oct 1995
Peer-reviewedYes
Externally publishedYes

External IDs

ORCID /0000-0001-7436-0103/work/142240247

Keywords

ASJC Scopus subject areas