papilionaceous


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papilionaceous

butterfly-like, as are the flowers of sweet-pea, broom etc.
References in periodicals archive ?
The simulated [S.sub.21] parameters of the papilionaceous dual-plane EBG before optimization are shown in Figure 10.
The design parameter of papilionaceous dual-plane EBG is v = [[R.sub.1] [R.sub.2] [R.sub.3] [r.sub.1] [r.sub.2] [r.sub.3]].
The modeling of the papilionaceous dual-plane EBG shows same conclusion with tapered dual-plane EBG which verifies superiority of the proposed KBNN.
Same with the optimization of tapered dual-plane EBG, the papilionaceous dual-plane EBG is optimized by exploiting the trained KBNN with 2K7H structure.
In this example, the proposed KBNN is also proven to be a good way to optimize the papilionaceous dual-plane EBG.
As can be seen from Table 9, using the KBNN based on coarse mesh to optimize the papilionaceous dual-plane EBG also shortens the time of optimization and makes it easy to optimize.
The modeling results of tapered dual-plane EBG and papilionaceous dual-plane EBG show that the knowledge-based neurons in the hidden layer can significantly reduce the number of hidden neurons which makes the structure simpler and the test results are in good accordance with the results of HFSS simulation which shows the strong generalization ability of the proposed KBNN.
Caption: Figure 9: Structure of papilionaceous dual-plane EBG.
Caption: Figure 10: Simulated [S.sub.21] parameter of papilionaceous dual-plane EBG.
Caption: Figure 11: Test sample result of papilionaceous dual-plane EBG.
Caption: Figure 13: Comparison of papilionaceous dual-plane EBG before and after optimization.
On the agency of bees in the fertilisation of papilionaceous flowers, and on the crossing of kidney beans.