The Vision of Autonomic Computing
Computer
A Multi-Agent Systems Approach to Autonomic Computing
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
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Neural dynamics coupled by adaptive synaptic information transmission provide a very powerful tool for biologically inspired visual processing systems[4]. Currently, progress is limited by the computing time needed to evaluate the underlying equations and by the high number of parameters necessary to tune to achieve the desired system performance. In this contribution we apply Autonomic Computing techniques to overcome these limitations. We approach the computing time problem with an error model of the differential equations allowing for self-optimization of the evaluation step size and the parameter problem with a self-configuration heuristics to keep neural activation in working range. We show the equivalence of system behavior compared to the case without self-management, the performance gain achieved by the self-optimization and the stability achieved by the self-configuration.