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Titlebook: Artificial Immune Systems; 8th International Co Paul S. Andrews,Jon Timmis,Andy M. Tyrrell Conference proceedings 2009 Springer-Verlag Berl

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31#
發(fā)表于 2025-3-26 21:06:26 | 只看該作者
32#
發(fā)表于 2025-3-27 03:00:13 | 只看該作者
Non-deterministic Explanation of Immune Responses: A Computer Model inflammation as the defining phenomena of an immune response. Whereas the theory recognizes that microscopic events such as molecular recognition are part of the dynamics of the system, it maintains that immune phenomena cannot be understood from the study of individual events alone. The theoretical basis for this work is described elsewhere [5].
33#
發(fā)表于 2025-3-27 08:53:44 | 只看該作者
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發(fā)表于 2025-3-27 19:52:40 | 只看該作者
Artificial Immune System Applied to the Multi-stage Transmission Expansion Planningproposed methodology includes the search for the least cost solution, bearing in mind investments and ohmic transmission losses. The multi-stage nature of the TEP will be also taken into consideration. Case studies on a small test system and on a real sub-transmission network are presented and discussed.
37#
發(fā)表于 2025-3-27 23:02:06 | 只看該作者
38#
發(fā)表于 2025-3-28 02:25:54 | 只看該作者
,Physikalische Gr??en und Messungen,viour of such systems is not solely determined by the aggregate behaviour of its components, but by emergent effects. The divide-and-conquer strategy is insufficient as the whole is greater than the sum of the parts.
39#
發(fā)表于 2025-3-28 07:45:33 | 只看該作者
,Spezielle Relativit?tstheorie,e the statistical and immunological aspects of AIRS and consider their merits individually. This perspective allows us to clarifying why AIRS performs as it does and identify deficiencies that leave AIRS lacking. A comparison with Radial Basis Functions suggests that each may have something to offer the other.
40#
發(fā)表于 2025-3-28 12:06:20 | 只看該作者
Conclusion: Toward an Ecology of Attentiontor signalling have a natural interpretation as Parzen Window Kernel Density Estimation applied to anomaly detection. We then demonstrate how the dynamic nature of the receptors allows anomaly detection when probability distributions vary in time.
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