Dynamic System Modelling from Data: Emerging Algorithms and Applications

Dear Colleagues,

Identification techniques for modelling from data, rather than from physical and chemical principles, usually include data processing, model structure detection, model parameter estimation, and post-validation. With fast-changing technology and ever-increasing computing capacity, many emerging algorithms in the fields of machine learning, big data, soft-sensor techniques, and reinforcement learning can realistically find applications in the identification of modern systems, ranging from manmade (engineering) to natural domains. On the other hand, no matter whatever algorithm is considered, some inherent issues must be overcome in one way or another, such as the proper handling of data uncertainty due to imperfect measurements that result in the presence of noise, time-delays, and data losses. Hence, a current challenge is to develop identification algorithms that will yield compact mathematical models which are useful for providing simple solutions to complex problems within a rigorous analytical framework.

The aim of this Special Issue is to report emerging novel identification algorithms for system modelling from data. The Editors welcome submissions in form of regular technical reports, comprehensive surveys, and case studies.

Specific topics of interest include but are not limited to:

• Novel identification algorithms for systems with time-delays.
• Recent developments of machine learning algorithms and neural networks.
• Modelling, analysis, and intelligent control of dynamic systems.
• Algorithms with enhanced knowledge for intelligent automation.
• Large-scale system: structure detection/construction and parameter estimation.
• Networked control system identification.
• Neural-fuzzy, and other inductive algorithms in theory and/or applications.

Prof. Dr. Quanmin Zhu
Prof. Dr. Jing Chen
Dr. Ya Gu
Guest Editors

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