Abstract
Many modern control systems, e.g., in automotive or robotic applications get increasingly complex and hard to design. This is due to the complex interactions of their internal subsystems, but additionally, these systems operate in a dynamically changing, complex environment. The Organic Computing (OC) initiative tries to cope with the resulting engineering demands by introducing emergence and self-x properties into the systems (e.g., self-organization, self-optimization). Within this context, we focus on control systems which adapt their behavior autonomously by learning.
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Rosemann, N., Brockmann, W. (2007). Concept for Controlled Self-optimization in Online Learning Neuro-fuzzy Systems. In: Hertzberg, J., Beetz, M., Englert, R. (eds) KI 2007: Advances in Artificial Intelligence. KI 2007. Lecture Notes in Computer Science(), vol 4667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74565-5_49
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DOI: https://doi.org/10.1007/978-3-540-74565-5_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74564-8
Online ISBN: 978-3-540-74565-5
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