K
Kit Yan Chan
Researcher at Curtin University
Publications - 192
Citations - 3621
Kit Yan Chan is an academic researcher from Curtin University. The author has contributed to research in topics: Fuzzy logic & New product development. The author has an hindex of 32, co-authored 177 publications receiving 3073 citations. Previous affiliations of Kit Yan Chan include Hong Kong Polytechnic University & University of Nottingham Malaysia Campus.
Papers
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Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm
TL;DR: A novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting.
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Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications
TL;DR: A new hybrid particle swarm optimization that incorporates a wavelet-theory-based mutation operation is proposed that significantly outperforms the existing methods in terms of convergence speed, solution quality, and solution stability.
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Improved Hybrid Particle Swarm Optimized Wavelet Neural Network for Modeling the Development of Fluid Dispensing for Electronic Packaging
TL;DR: An improved hybrid particle swarm optimization (PSO)-based wavelet neural network (WNN) for modeling the development of fluid dispensing for electronic packaging (MFD-EP) is presented, which incorporates a wavelet-theory-based mutation operation.
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Psychophysiology-Based QoE Assessment: A Survey
Ulrich Engelke,Daniel P. Darcy,Grant H. Mulliken,Sebastian Bosse,Maria G. Martini,Sebastian Arndt,Jan-Niklas Antons,Kit Yan Chan,Naeem Ramzan,Kjell Brunnström +9 more
TL;DR: A survey of psychophysiology-based assessment for quality of experience (QoE) in advanced multimedia technologies provides a classification of methods relevant to QoE and describes related psychological processes, experimental design considerations, and signal analysis techniques.
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A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach
TL;DR: In this study, a new methodology of generating customer satisfaction models using a neuro-fuzzy approach is proposed and results suggested that the proposed approach outperformed the statistical regression method in terms of mean absolute errors and variance of errors.