Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010 |
| Pages | 840-845 |
| Number of pages | 6 |
| Publication status | Published - 2010 |
| Event | 9th International Conference on Machine Learning and Applications, ICMLA 2010 - Washington, DC, United States Duration: 12 Dec 2010 → 14 Dec 2010 |
Publication series
| Name | Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010 |
|---|
Abstract
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 [8]. 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 databased 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.
Keywords
- Agent-based evolutionary computation, Memetic optimization algorithms, System identification
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Human-Computer Interaction
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Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010. 2010. p. 840-845 5708953 (Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - System identification with multi-agent-based evolutionary computation using a local optimization kernel
AU - Bohlmann, Sebastian
AU - Klinger, Volkhard
AU - Szczerbicka, Helena
PY - 2010
Y1 - 2010
N2 - 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 [8]. 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 databased 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.
AB - 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 [8]. 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 databased 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.
KW - Agent-based evolutionary computation
KW - Memetic optimization algorithms
KW - System identification
UR - http://www.scopus.com/inward/record.url?scp=79952399548&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2010.130
DO - 10.1109/ICMLA.2010.130
M3 - Conference contribution
AN - SCOPUS:79952399548
SN - 9780769543000
SN - 978-1-4244-9211-4
T3 - Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
SP - 840
EP - 845
BT - Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
T2 - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Y2 - 12 December 2010 through 14 December 2010
ER -