Abstract
Self-Managing systems are a significant feature in Autonomic Computing which is required for system reliability and performance in a changing environment. The work described in this book chapter is concerned with self-healing systems; systems that can detect and analyse issues with their behavior and performance, and fixe or reconfigure as appropriate. These processes should occur in real-time to restore the desired functionality as soon as possible. The system should ideally maintain functionality during the healing process which occurs at runtime. Adaptive neural networks are proposed as a solution to some of these challenges; monitoring the system and environment, mapping a suitable solution and adapting the system accordingly. A novel application of a modified Pipelined Recurrent Neural Network is proposed in this chapter with experiments aimed to assess its applicability to online.
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AI-Jumeily, D., Al-Zawi, M., Hussain, A.J., Dobre, C. (2014). Adaptive Pipelined Neural Network Structure in Self-aware Internet of Things. In: Bessis, N., Dobre, C. (eds) Big Data and Internet of Things: A Roadmap for Smart Environments. Studies in Computational Intelligence, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-319-05029-4_5
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DOI: https://doi.org/10.1007/978-3-319-05029-4_5
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