System Identification with Multi-Agent-based Evolutionary Computation Using a Local Optimization Kernel

Most technical and manufacturing processes are based on an empiric process understanding, there only very incomplete formal relations exist. To establish a process model, the identification of the appropriate process is essential. In addition, this process model has to feature a quality of execution to enable forward-looking properties like an online prediction mode. This report argues that the agent-based identification is appropriate to this modelling issue. Although there were many predecessor approaches, which tried to design formal models of manufacturing processes, all of them fell short of the data based identification of complex systems, like paper manufacturing: complex systems consisting of continuous and discrete parts, called hybrid manufacturing systems. This paper focuses on the system identification with agent based evolutionary computation using a local optimization kernel. It presents the system architecture and introduces a data based identification method with different local optimization algorithms. Finally we consider the characteristics of an identification framework with large-scale data processing. We close with identification results related to the 2-step optimization algorithm.