The University of Nottingham · School of Computer Science

Dr Chao Chen

Member of the Laboratory for Uncertainty in Data and Decision Making (LUCID) and the Intelligent Modelling and Analysis (IMA) Research Group. His main research interests lie in the development of artificial intelligence techniques in the area of fuzzy sets and systems. He is particularly interested in optimisation and evaluation — focusing on how better performance can be achieved across broad criteria (e.g. accuracy, efficiency, interpretability, and explainability), and how such improvements can be more accurately measured.

Fuzzy SystemsType-2 FuzzyDeep LearningInterpretabilityXAIFuzzyR

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Publication ChairFUZZ-IEEE @ WCCI 2026, QAI 2026

Associate EditorIEEE Computational Intelligence Magazine

Special Session Co-organiserAdvances in Optimisation for Fuzzy Systems @ FUZZ-IEEE / IEEE WCCI 2026, Maastricht

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Latest News

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Dec 2025

Feb 2026

Jan 2026

Dec 2025

Selected Publications

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C Chen, J Twycross, JM Garibaldi · PLOS ONE 2017

Time SeriesEvaluation MetricsForecasting

DOI ↗

C Chen, JM Mendel, JM Garibaldi · IEEE Transactions on Fuzzy Systems 2025

Fuzzy SystemsOptimisationMembership Functions

Code ↗DOI ↗

F Abbasov, C Chen, JM Garibaldi · IEEE International Conference on Fuzzy Systems 2025

Fuzzy SystemsMembership FunctionsDeep Learning

DOI ↗

Q Lin, X Chen, C Chen, JM Garibaldi · Information Sciences 2024

Medical AIFuzzy Rough SetsImage Segmentation

DOI ↗

C Chen, D Wu, JM Garibaldi, RI John, et al. · IEEE Transactions on Fuzzy Systems 2021

Type-2 FuzzyFuzzy SystemsAlgorithms

Code ↗DOI ↗

Open-Source Software

FuzzyRon CRAN

An open-source Fuzzy Logic Toolkit for the R programming language. Supports design, simulation, and optimisation of type-1 and interval type-2 fuzzy inference systems — used internationally for research and teaching.

Type-1 & Interval Type-2 FISANFIS OptimisationNon-Singleton FuzzificationHierarchical Fuzzy SystemsAccuracy Measures (UMBRAE)Interactive GUIAutomatic Differentiation

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Related Publications

example.R

library(FuzzyR)
 
# Build a Type-1 Fuzzy Inference System
fis <- newfis("temperature")
fis <- addvar(fis, "input",  "temp", c(0, 40))
fis <- addvar(fis, "output", "fan",  c(0, 100))
 
# Define membership functions
fis <- addmf(fis, "input", 1, "cold", "trimf", c(-10,  0, 15))
fis <- addmf(fis, "input", 1, "warm", "trimf", c( 10, 22, 30))
fis <- addmf(fis, "input", 1, "hot",  "trimf", c( 25, 40, 50))
 
# Add rules and evaluate
fis <- addrule(fis, c(1,1,1,1))
evalfis(25, fis)   # → 65.0