World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
8093
World Ranking
5697
National Ranking
755

Overview

Min Han is affiliated with Dalian University of Technology in China and has contributed extensively to research in computer science and engineering. Their work focuses predominantly on artificial intelligence, computer vision and pattern recognition, control and systems engineering, signal processing, and electrical and electronic engineering.

The main topics that Min Han covers through their research include:

  • Neural networks and applications
  • Neural networks and reservoir computing
  • Time series analysis and forecasting
  • Machine fault diagnosis techniques
  • Fault detection and control systems
  • Remote-sensing image classification
  • Machine learning and extreme learning machines (ELM)

Min Han has published in various scientific venues, with frequent appearances in:

  • SSRN Electronic Journal
  • IEEE Transactions on Neural Networks and Learning Systems
  • Measurement
  • Engineering Applications of Artificial Intelligence
  • International Journal of Remote Sensing

Several recent papers authored or co-authored by Min Han include:

  • "Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction," 2020, IEEE Transactions on Neural Networks and Learning Systems
  • "Advancing green extraction of bioactive compounds using deep eutectic solvent-based ultrasound-assisted matrix solid-phase dispersion: Application to UHPLC-PAD analysis of alkaloids and organic acids in Coptidis rhizoma," 2024, Talanta
  • "A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation," 2020, Neurocomputing (co-authored by Nannan Lu)
  • "A hybrid prognostic strategy with unscented particle filter and optimized multiple kernel relevance vector machine for lithium-ion battery," 2020, Measurement (co-authored by Xiaofei Sun)
  • "Modified BBO-Based Multivariate Time-Series Prediction System With Feature Subset Selection and Model Parameter Optimization," 2020, IEEE Transactions on Cybernetics (co-authored by Xiaodong Na)

Frequent collaborators in Min Han's research include:

  • Weijie Ren
  • Chengkun Zhang
  • Xinghan Xu
  • Nannan Lu
  • Xiaodong Na

Best Publications

  • Output-Feedback Cooperative Formation Maneuvering of Autonomous Surface Vehicles With Connectivity Preservation and Collision Avoidance

    Zhouhua Peng;Dan Wang;Tieshan Li;Min Han

  • Chaotic Time Series Prediction Based on a Novel Robust Echo State Network

    Decai Li;Min Han;Jun Wang

  • Support Vector Echo-State Machine for Chaotic Time-Series Prediction

    Zhiwei Shi;Min Han

  • Prediction of chaotic time series based on the recurrent predictor neural network

    Min Han;Jianhui Xi;Shiguo Xu;Fu-Liang Yin

  • Recurrent Broad Learning Systems for Time Series Prediction

    Meiling Xu;Min Han;C. L. Philip Chen;Tie Qiu

  • Online sequential extreme learning machine with kernels for nonstationary time series prediction

    Xinying Wang;Min Han

  • A Review on Intelligence Dehazing and Color Restoration for Underwater Images

    Min Han;Zhiyu Lyu;Tie Qiu;Meiling Xu

  • BitTableFI: An efficient mining frequent itemsets algorithm

    Jie Dong;Min Han

  • Noise Smoothing for Nonlinear Time Series Using Wavelet Soft Threshold

    Min Han;Yuhua Liu;Jianhui Xi;Wei Guo

  • Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation

    Jianchao Fan;Min Han;Jun Wang

  • Data-driven based fault prognosis for industrial systems: a concise overview

    Kai Zhong;Min Han;Bing Han

  • Adaptive Elastic Echo State Network for Multivariate Time Series Prediction

    Unknown

  • Structured Manifold Broad Learning System: A Manifold Perspective for Large-Scale Chaotic Time Series Analysis and Prediction

    Min Han;Shoubo Feng;C. L. Philip Chen;Meiling Xu

  • Generalized Single-Hidden Layer Feedforward Networks for Regression Problems

    Ning Wang;Meng Joo Er;Min Han

  • Backpropagating Constraints-Based Trajectory Tracking Control of a Quadrotor With Constrained Actuator Dynamics and Complex Unknowns

    Ning Wang;Shun-Feng Su;Min Han;Wen-Hua Chen

  • Laplacian Echo State Network for Multivariate Time Series Prediction

    Min Han;Meiling Xu

  • Parsimonious extreme learning machine using recursive orthogonal least squares.

    Ning Wang;Meng Joo Er;Min Han

  • Interval Type-2 Fuzzy Neural Networks for Chaotic Time Series Prediction: A Concise Overview

    Min Han;Kai Zhong;Tie Qiu;Bing Han

  • Analysis and modeling of multivariate chaotic time series based on neural network

    M. Han;Y. Wang

  • Remote Sensing Image Classification Based on Ensemble Extreme Learning Machine With Stacked Autoencoder

    Fei Lv;Min Han;Tie Qiu

  • A Dynamic Feedforward Neural Network Based on Gaussian Particle Swarm Optimization and its Application for Predictive Control

    Min Han;Jianchao Fan;Jun Wang

  • Magnetic Induction Tomography

    Yuyan Xue;Min Han

Frequent Co-Authors

Tie Qiu
Tie Qiu Tianjin University
Jun Wang
Jun Wang City University of Hong Kong
Ning Wang
Ning Wang Dalian Maritime University
Meng Joo Er
Meng Joo Er Dalian Maritime University
C. L. Philip Chen
C. L. Philip Chen South China University of Technology
Dan Wang
Dan Wang Dalian Maritime University
Tieshan Li
Tieshan Li University of Electronic Science and Technology of China
Zhouhua Peng
Zhouhua Peng Dalian Maritime University
Dongbin Zhao
Dongbin Zhao Chinese Academy of Sciences
Wen-Hua Chen
Wen-Hua Chen Loughborough University

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