A coupled schmitt trigger oscillator neural network for pattern recognition applications

Ting Zhang, Mohammad R. Haider, Iwan D. Alexander, Yehia Massoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper demonstrates a coupled Schmitt trigger oscillator based oscillator neural network (SMT-ONN) for pattern recognition applications. Unlike previous ONN models, the SMT-ONN can be easily realized in both hardware and software levels. A mathematical model of the Schmitt Trigger Oscillator as well as the corresponding CMOS circuit are presented to validate the mathematical model. The SMT-ONN can realize the pattern recognition task by considering the convergence time and frequency as the recognition indicators. A Kuramoto model based frequency synchronization approach is utilized, and simulation results indicate less than 160 ms convergence time and close frequency match for a simplified pattern recognition application.
Original languageEnglish (US)
Title of host publicationMidwest Symposium on Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)9781538673928
StatePublished - Jan 22 2019
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13


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