World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
41
Citations
4308
World Ranking
4371
National Ranking
669

Ridong Zhang publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Ridong Zhang sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 163 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Ridong Zhang D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Ridong Zhang sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 41 D-Index — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Overview

Ridong Zhang is affiliated with Hangzhou Dianzi University in China and has a significant body of research primarily within the field of engineering. Their work is especially concentrated on several subfields, including Control and Systems Engineering, Automotive Engineering, Electrical and Electronic Engineering, Mechanical Engineering, and Materials Chemistry.

Their research covers a variety of topics, with a focus on:

  • Advanced Control Systems Optimization
  • Fault Detection and Control Systems
  • Iterative Learning Control Systems
  • Advanced Battery Technologies Research
  • Advanced Combustion Engine Technologies
  • Electric and Hybrid Vehicle Technologies
  • Mineral Processing and Grinding

Ridong Zhang has contributed to numerous scientific publications, with frequent appearances in journals such as:

  • Industrial & Engineering Chemistry Research
  • Fuel
  • Measurement and Control
  • Chemical Engineering Science
  • Journal of Process Control

Some of their recent papers include:

  • "RBF neural network modeling approach using PCA based LM-GA optimization for coke furnace system," 2021, Applied Soft Computing
  • "Fault Diagnosis of Complex Chemical Processes Using Feature Fusion of a Convolutional Network," 2021, Industrial & Engineering Chemistry Research
  • "Hydrogen production from ammonia-rich combustion for fuel reforming under high temperature and high pressure conditions," 2022, Fuel
  • "Enhanced Q-learning for real-time hybrid electric vehicle energy management with deterministic rule," 2020, Measurement and Control
  • "Constrained model predictive fault-tolerant control for multi-time-delayed batch processes with disturbances: A Lyapunov-Razumikhin function method," 2021, Journal of the Franklin Institute

Throughout their career, Zhang has collaborated frequently with a number of co-authors, including:

  • Furong Gao
  • Jili Tao
  • Limin Wang
  • Yunliang Qi
  • Longhua Ma

Best Publications

  • A Nonlinear Fuzzy Neural Network Modeling Approach Using an Improved Genetic Algorithm

    Ridong Zhang;Jili Tao

  • Fuzzy Optimal Energy Management for Fuel Cell and Supercapacitor Systems Using Neural Network Based Driving Pattern Recognition

    Ridong Zhang;Jili Tao;Huiyu Zhou

  • Improved fuzzy PID controller design using predictive functional control structure.

    Yuzhong Wang;Qibing Jin;Ridong Zhang

  • A New Design of Model Predictive Tracking Control for Networked Control System Under Random Packet Loss and Uncertainties

    Renquan Lu;Yong Xu;Ridong Zhang

  • New Minmax Linear Quadratic Fault-Tolerant Tracking Control for Batch Processes

    Ridong Zhang;Renquan Lu;Anke Xue;Furong Gao

  • Support vector machine based predictive functional control design for output temperature of coking furnace

    Ridong Zhang;Shuqing Wang

  • An improved model predictive control approach based on extended non-minimal state space formulation

    Ridong Zhang;Anke Xue;Shuqing Wang;Zhengyun Ren

  • Intelligent Fault Diagnosis for Chemical Processes Using Deep Learning Multimodel Fusion.

    Nan Wang;Fan Yang;Ridong Zhang;Furong Gao

  • Temperature Control of Industrial Coke Furnace Using Novel State Space Model Predictive Control

    Ridong Zhang;Anke Xue;Furong Gao

  • Nonlinear Monotonically Convergent Iterative Learning Control for Batch Processes

    Jingyi Lu;Zhixing Cao;Ridong Zhang;Furong Gao

  • Data-Driven Modeling Using Improved Multi-Objective Optimization Based Neural Network for Coke Furnace System

    Ridong Zhang;Jili Tao

  • Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems

    Ridong Zhang;Jili Tao;Renquan Lu;Qibing Jin

  • GA-Based Fuzzy Energy Management System for FC/SC-Powered HEV Considering H 2 Consumption and Load Variation

    Ridong Zhang;Jili Tao

  • Model Fusion and Multiscale Feature Learning for Fault Diagnosis of Industrial Processes

    Unknown

  • State space model predictive fault-tolerant control for batch processes with partial actuator failure

    Ridong Zhang;Ridong Zhang;Jingyi Lu;Hongyi Qu;Furong Gao

  • Modeling and nonlinear predictive functional control of liquid level in a coke fractionation tower

    Ridong Zhang;Anke Xue;Shuqing Wang

  • Iterative learning fault-tolerant control for injection molding processes against actuator faults

    Limin Wang;Limin Wang;Fanfan Liu;Jingxian Yu;Ping Li

  • An improved state-space model structure and a corresponding predictive functional control design with improved control performance

    Ridong Zhang;Anke Xue;Shuqing Wang;Jianming Zhang

  • Dynamic Modeling and Nonlinear Predictive Control Based on Partitioned Model and Nonlinear Optimization

    Ridong Zhang;Anke Xue;Shuqing Wang

  • Design of dynamic matrix control based PID for residual oil outlet temperature in a coke furnace

    Sheng Wu;Ridong Zhang;Ridong Zhang;Renquan Lu;Furong Gao

  • A Systematic Min–Max Optimization Design of Constrained Model Predictive Tracking Control for Industrial Processes against Uncertainty

    Ridong Zhang;Sheng Wu;Zhixing Cao;Jingyi Lu

  • A New Approach of Takagi–Sugeno Fuzzy Modeling Using an Improved Genetic Algorithm Optimization for Oxygen Content in a Coke Furnace

    Ridong Zhang;Ridong Zhang;Jili Tao;Furong Gao

Frequent Co-Authors

Furong Gao
Furong Gao Hong Kong University of Science and Technology
Renquan Lu
Renquan Lu Guangdong University of Technology
Zheng-Guang Wu
Zheng-Guang Wu Zhejiang University
Xudong Zhao
Xudong Zhao Dalian University of Technology
Huiyu Zhou
Huiyu Zhou University of Leicester

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