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The biological plausibility and potential computational power [18] of the dynamics update (2) inspired both early computer [19] and neural network architectures [4, 20].
It follows that the optimal setting (7) for x minimizing probability flow gives robust storage (with a single parallel dynamics update) of all k-cliques for (p < 1/4).
To guarantee that all edges e in the clique are labeled 1 after one dynamics update, we need (x > frac{1}{N e)}); that is, x > frac{1}{2k + o(sqrt{k}ln k)}.
This article studies iterative learning control (ILC) where multiple heterogeneous linear subsystems (with potentially different individual dynamics) update their input simultaneously based on the error in a collaboratively controlled desired output.
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In summary, we can say that stochastic changes enter into the proposed simulation model in the form: (i) unidirectional updates of the cooperation level co l described by the Equations 10 and 12 and (ii) extremal dynamics updates involved in Equations 17 and 18. Multitude of simulation studies exists where the generators of uniform distributions are commonly used.
The main concept of sequential Nash bargaining is to observe the current system environment and dynamically update the NBS to adapt to the network dynamics.
Potential discrepancies or ambiguities in the local fault diagnosis results, which may be caused by the strong dynamic coupling between the individual subsystems and the presence of plant-model mismatch, are reconciled by means of a fault estimation confidence interval which is obtained by analyzing the networked closed-loop dynamics at update times.
The serial acquisition of each voxel used to construct a 3D image is a slow process, preventing application of SHG microscopy to rapid dynamics, restricting update rates in a 3D volume to well below 1 Hz.
The dynamics is updated at an event (generating synaptic currents and resetting at firing events, changing the gating dynamics at switching events, and releasing from reset at refractory events), and the process repeated.
In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner.
Both growth and update dynamics clearly represent big challenges for search engines.
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