The phrase "adapting the network" is correct and usable in written English.
It can be used when discussing changes or modifications made to a network system to improve its performance or functionality.
Example: "The IT team is focused on adapting the network to better support remote work capabilities."
Alternatives: "modifying the network" or "adjusting the network".
Then, the generated schedule is continually updated over time using a runtime adaptation approach in order to be capable of dynamically adapting the network to changing conditions and mitigating the side effects of provisioned reliability.
Furthermore, the algorithm shall be able to adapt itself to new circumstances by adapting the network and the system parameters.
By providing easy data access thanks to the unique and location-independent content names, in-network caching and name-based routing, NDN expects to hide from IoT applications the complexity and diversity of the underlying Things by adapting the network operation to their features.
Therefore, methods that reduce energy consumption by adapting the network resource to the traffic demand are important research directions.
Self-optimization functions automatically modify network parameters during the operational phase in order to improve the network performance by adapting the network to changes of environment conditions.
SON has recently attracted interests from operators since SON provides capabilities such as significant reductions in CAPEX and OPEX during both network deployment and operation period, improvements in network quality and quality of experience for users and provides optimized performance from adapting the network to environments and other operational conditions.
The on-line learning is used to adapt the network under varying flight conditions.
An algorithm is derived, which adapts the network parameters according to the self-triggered sampler of every control loop.
Evolutionary algorithms are used to automatically adapt the network architecture and connection weights according to the problem environment without substantial human intervention.
Provisions have also been made to dynamically adapt the network to local traffic fluctuations by maintaining databases at some nodes and reallocation of unused resources, thereby improving its overall performance.
The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.
