Knowledge-based and data-driven fuzzy modeling for rockburst prediction[J] . Amoussou Coffi Adoko,Candan Gokceoglu,Li Wu,Qing Jun Zuo.International Journal of Rock Mechanics and Mining Sciences . 2013ADOKO A C;
Collective intelligence (CI) shows promising application prospects. Current research methodologies of intelligent decision making for CI systems can be categorized as knowledge-based and data-driven methods, both showing inherent advantages and disadvant
In the first set of experiments, we respectively use local search algorithm, expert knowledge, and hybrid method to train the BN models — hereafter termed as LS-BN, EK-BN, and H-BN respectively. We compare the Conclusion This study proposed a hybrid knowledge-based and data-driven approach...
Deep learning-based molecular generation has extensive applications in many fields, particularly drug discovery. However, the majority of current deep generative models are ligand-based and do not consider chemical knowledge in the molecular generation p
This journal focuses on systems that use knowledge-based (KB) techniques to support human decision-making, learning and action; emphases the practical significance of such KB-systems; its computer development and usage; covers the implementation of such KB-systems: design process, models and ...
and capabilities: to support human prediction and decision-making through data science and computation techniques; to provide a balanced coverage of both theory and practical study in the field; and to encourage new development and implementation of knowledge-based intelligence models, methods, systems,...
to support human prediction and decision-making through data science and computation techniques; to provide a balanced coverage of both theory and practical study in the field; and to encourage new development and implementation of knowledge-based intelligence models, methods, systems, and software tool...
Real-world applications often require the joint use of datadriven and knowledge-based models. While data-driven models are learned from available process data, knowledge-based models are able to provide additional information not contained in the data. In this contribution, we propose a method to ...
In recent years, causal inference has emerged as a critical tool for understanding cause-and-effect relationships within complex systems. By incorporating causal reasoning into machine learning, models can move beyond correlation-based learning to … ...
KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots In this work, we consider the problem of deriving and incorporating accurate dynamic models for model predictive control (MPC) with an application to quadrotor control. MPC relies on precise dynamic models to ...