Two experiments show that: (a) the proposed method has better forecasting performance than the traditional Autoregressive Integrated Moving Average (ARIMA) model, the Persistent Random Walk Model (PRWM) and the Back Propagation (BP) neural networks; and (b) the proposed method has satisfactory performance in both of the accuracy and the time performance.
This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions.
However, in order to achieve both interactivity and real time performance, the algorithm should run at a high frame rates, i.e. at least 60 frames per second.
The results show that HomTreeMiner has the best time performance on both XMark and CSlogs (Fig. 12b, c); it runs slightly slower than EmbTreeMiner on Treebank (Fig. 12a).
Although C_PBNA spends more time dealing with both probabilistic networks, its time performance is still acceptable.
We have presented two implementations of a new TMO on a GPU NVIDIA ION2 integrated in a small size netbook and on a Spartan 3 FPGA-based platform reaching in both cases real-time performance when working with 640 × 480 RGB images.
We evaluate the developed algorithm by comparing it to a previously developed approach based on constraint satisfaction (CS), both in terms of run time performance and quality of the solutions.
It can be seen from Table 1 that a significant improvement of both the availability performance and the outage time, practically two orders of magnitude (nearly 76 hours/year), could be achieved for the simulated hybrid FSO/RF system in comparison with the FSO system alone.
Simulation reveals the feasibility of the CBR approach and major improvements by the rule-based approach considering SLA violations, resource utilization, the number of necessary reconfigurations and time performance for both, synthetically generated and real-world data.
