Least Mean Square-Based Fuzzy c-Means Clustering for Load Recognition of Induction Heating

M. H. Qais, K. H. Loo, Junwei Liu, Cheung Ming Lai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


Currently, with the development of induction heating (IH) and artificial intelligence (AI), the domestic induction cooker (DIC) has become smarter and more reliable. DIC should be able to recognize the material and size of cookware, as well as be susceptible to users' misbehavior. This article utilizes AI techniques to recognize the cookware items and protect the users and systems from hazards. First, a laboratory prototype of DIC is used to collect the output voltage and current for different cookware items under different switching frequencies and temperatures. After that, the Gray Wolf Optimizer (GWO) algorithm is applied to estimate the equivalent resistance and inductance of all coil-cookware loads. Then, the fuzzy c-means (FCM) clustering algorithm and the least mean square (LMS) algorithm are combined and applied for clustering the unlabeled data. This article suggested four clusters, including non-ferromagnetic (NF) cookware, small-sized ferromagnetic (SF) cookware, medium-sized ferromagnetic (MF) cookware, and no cookware (NC) cluster. Finally, the proposed AI method is applied to the load recognition of a commercial DIC. By comparing the centers of the clusters with the estimated equivalent resistance and inductance of the coil-cookware load, the size and material of the cookware can be found.

Original languageEnglish
Article number9005510
Pages (from-to)1-10
JournalIEEE Transactions on Instrumentation and Measurement
Publication statusPublished - Aug 2022


  • Cookware recognition
  • domestic cooking
  • fuzzy c-means (FCM) clustering
  • induction heating (IH)
  • unsupervised learning

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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