Our method achieves almost perfect identification and segmentation in the case of switching chaotic dynamics where input manifolds overlap and input-output relations are ambiguous.
Both Figures 6 and 7 show that the proposed method achieves almost the same average rate as the conventional one, indicating that the proposed method can achieve the highest reward without sacrificing much transmission rate.
By comparison, the proposed method achieves almost the same results as the Bresson et al.'s method (see Figure 8e,f,g,h); however, we observe from Table 3 that the iteration numbers and CPU times of the proposed model are less than the Bresson et al.'s method.
However, with N v = 512, both methods achieve almost identical PAPR reduction gain.
As all of our four projection methods achieve almost the same results, here, we only give the experimental data for SBPM in the last row of Figure 5.
The GSS-based method improved all error rates compared with the power SS-based method and achieved almost the same word insertion error as CMN.
From Figs. 13 and 14, it can be found that, the performance of active damping method by building ( R_{v} ) is better than the active damping method by building ( C_{v} ), because the former method can achieve almost the same damping effect with relatively smaller undesirable oscillation in rotating speed.
Compared with the existing approaches, the proposed method can achieve almost the same performance, but with significant improvement in computational complexity.
The speed method also achieves almost as good transmission rate as the proposed one, but at a price of reduced reward performance.
On the other hand, the greedy approach achieved almost the same SSSNR performance with much fewer filter designs, being more efficient than the combinatorial method.
