Digital Signal Processing: A Practical Approach
Digital Signal Processing: A Practical Approach
The application of a dendritic cell algorithm to a robotic classifier
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Articulation and clarification of the dendritic cell algorithm
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Quiet in class: classification, noise and the dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
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In previous work we derived a mathematical model which allows the frequency response of a dendritic cell to be predicted. The model has three, key limitations: the model assumes that the intermediate co stimulatory molecule signal is constant; it is only possible to make predictions for a single cell and the model only takes into account the signal processing element of the dendritic cell algorithm, with no attempt to explore the antigen presenting phase. In this paper we explore the original model and attempt to extend it to include the effects of multiple cells. It is found that the complex interactions between the cells creates a one to many relationship between the input frequency and the output frequency. This suggests that traditional frequency-based techniques alone are unlikely to yield an effective automated tuning mechanism.