What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Mathematical optimization, Data envelopment analysis, Fuzzy logic, Energy and Data mining.
His studies in Data envelopment analysis integrate themes in fields like Slack variable and Biochemical engineering.
His studies deal with areas such as Extreme learning machine and Sensor fusion as well as Fuzzy logic.
His Extreme learning machine study incorporates themes from Algorithm and Nonlinear system.
His Energy research includes themes of Particle swarm optimization, Small data, Monte Carlo method and Synthetic data.
The Data mining study combines topics in areas such as Artificial neural network, Robustness and Artificial intelligence.
His most cited work include:
- Review: Multi-objective optimization methods and application in energy saving (179 citations)
- Review: Multi-objective optimization methods and application in energy saving (179 citations)
- Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry (85 citations)
What are the main themes of his work throughout his whole career to date?
Qunxiong Zhu focuses on Data mining, Extreme learning machine, Artificial intelligence, Chemical process and Artificial neural network.
His studies in Extreme learning machine integrate themes in fields like Stability, Energy, Fuzzy logic and Nonlinear system.
His Energy study incorporates themes from Mathematical optimization, Reduction, Process engineering and Biochemical engineering.
He interconnects Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence.
His Chemical process research is multidisciplinary, incorporating perspectives in Principal component analysis, Simulation and Process.
Qunxiong Zhu focuses mostly in the field of Artificial neural network, narrowing it down to matters related to Algorithm and, in some cases, Pearson product-moment correlation coefficient.
He most often published in these fields:
- Data mining (48.56%)
- Extreme learning machine (39.90%)
- Artificial intelligence (33.17%)
What were the highlights of his more recent work (between 2020-2021)?
- Data mining (48.56%)
- Function (5.29%)
- Interpolation (8.17%)
In recent papers he was focusing on the following fields of study:
His main research concerns Data mining, Function, Interpolation, Soft sensor and Scheduling.
His Data mining research overlaps with other disciplines such as PrefixSpan, Term, Scale, Ambiguity and Flood myth.
His Function investigation overlaps with other disciplines such as Benchmark, Artificial neural network, Computational intelligence, Conditional probability distribution and Algorithm.
His Interpolation research incorporates themes from Isomap, Nonlinear dimensionality reduction, Small data and Key.
His study on Soft sensor is intertwined with other disciplines of science such as Sampling, Logarithm, Measure and Centroidal Voronoi tessellation.
His Scheduling research is included under the broader classification of Mathematical optimization.
Between 2020 and 2021, his most popular works were:
- Novel manifold learning based virtual sample generation for optimizing soft sensor with small data (3 citations)
- Novel manifold learning based virtual sample generation for optimizing soft sensor with small data (3 citations)
- Integrating virtual sample generation with input-training neural network for solving small sample size problems: application to purified terephthalic acid solvent system (1 citations)
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
