What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial neural network, Artificial intelligence, Data mining, Principal component analysis and Machine learning.
His Artificial neural network study combines topics in areas such as Control engineering, Process control, Algorithm and Optimal control.
His biological study spans a wide range of topics, including Process engineering and Polymer.
The study incorporates disciplines such as Analysis of covariance, Point cloud, Multivariate statistics and Projection in addition to Principal component analysis.
His study in Machine learning is interdisciplinary in nature, drawing from both Fault, Majority rule and Range.
His study explores the link between Fuzzy logic and topics such as Network model that cross with problems in Neuro-fuzzy, Defuzzification, Control theory, Model predictive control and Fuzzy control system.
His most cited work include:
- Recurrent neuro-fuzzy networks for nonlinear process modeling (230 citations)
- Performance monitoring of processes with multiple operating modes through multiple PLS models (125 citations)
- Developing robust non-linear models through bootstrap aggregated neural networks (117 citations)
What are the main themes of his work throughout his whole career to date?
Jie Zhang mainly investigates Artificial neural network, Artificial intelligence, Control theory, Control engineering and Process control.
Jie Zhang has included themes like Mathematical optimization, Optimal control, Data mining and Nonlinear system in his Artificial neural network study.
Jie Zhang has researched Nonlinear system in several fields, including Process modeling and Algorithm.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Pattern recognition.
His work in the fields of Machine learning, such as Principal component regression, overlaps with other areas such as Test data.
His work investigates the relationship between Control theory and topics such as Distillation that intersect with problems in Efficient energy use.
He most often published in these fields:
- Artificial neural network (30.05%)
- Artificial intelligence (25.25%)
- Control theory (23.99%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (25.25%)
- Artificial neural network (30.05%)
- Pattern recognition (8.84%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Artificial neural network, Pattern recognition, Algorithm and Data mining are his primary areas of study.
His Artificial intelligence research includes themes of Machine learning and Process.
His Artificial neural network research incorporates themes from Cuckoo search, Soft sensor, Principal component analysis, Process engineering and Reinforcement learning.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Spectral clustering and Feature.
Jie Zhang combines subjects such as Subspace topology, Segmentation, Point cloud, Genetic algorithm and Benchmark with his study of Algorithm.
As a part of the same scientific family, Jie Zhang mostly works in the field of Data mining, focusing on Nonlinear system and, on occasion, Robustness.
Between 2017 and 2021, his most popular works were:
- Menin Deficiency Leads to Depressive-like Behaviors in Mice by Modulating Astrocyte-Mediated Neuroinflammation. (44 citations)
- Synergistic neuroprotection by coffee components eicosanoyl-5-hydroxytryptamide and caffeine in models of Parkinson's disease and DLB. (27 citations)
- Synergistic neuroprotection by coffee components eicosanoyl-5-hydroxytryptamide and caffeine in models of Parkinson's disease and DLB. (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Algorithm, Pattern recognition, Neuroscience and Subspace topology.
His studies deal with areas such as Point cloud, Estimator, Noise and Evolution strategy as well as Algorithm.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Extreme learning machine and Feature.
His work focuses on many connections between Subspace topology and other disciplines, such as Feature learning, that overlap with his field of interest in Singular value decomposition, Algorithm design, Time complexity, Principal component analysis and Online algorithm.
His Deep learning study frequently links to adjacent areas such as Artificial neural network.
His Artificial neural network research is classified as research in Machine learning.
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