University of Toyama | Japan
Mr. Zhang Zhenqian is a dedicated researcher whose work bridges artificial intelligence, machine learning, and meteorology, with an emphasis on developing advanced neural network models for predictive analytics. His recent publication, “RD2: Reconstructing the Residual Sequence via Under Decomposing and Dendritic Learning for Generalized Time Series Predictions,” featured in Neurocomputing (October 2025), showcases his innovative approach to enhancing time series forecasting accuracy through the integration of dendritic learning mechanisms and residual sequence reconstruction. Collaborating with Houtian He, Zhenyu Lei, Zihang Zhang, and Shangce Gao, Mr. Zhang contributes to advancing the computational intelligence field by addressing challenges in dynamic data modeling and predictive reliability. His research explores the intersection of data-driven modeling and environmental systems, offering valuable insights for improving real-world forecasting, particularly in meteorological and environmental applications. With a growing scholarly presence and contributions recognized through peer-reviewed international publications, Mr. Zhang exemplifies a new generation of researchers committed to interdisciplinary innovation. His work not only strengthens the theoretical foundations of artificial intelligence but also demonstrates its transformative potential in understanding and managing complex natural and engineered systems.
Profile : Orcid
Featured Publication
Zhang, Z., He, H., Lei, Z., Zhang, Z., & Gao, S. (2025). RD2: Reconstructing the residual sequence via under decomposing and dendritic learning for generalized time series predictions. Neurocomputing, 131867.