asymptotic direction

asymptotic direction的基本解释

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渐近方向

与asymptotic direction相关的网络例句 [注：此内容来源于网络，仅供参考]

Algorithm application of neural networks.Ⅲ Implementation of neuro-computers. The main contribution of the dissertation can be summarized as follows: 1 Hopf bifurcation of three kind of neural networks are discussed in detail, including type of discrete time delay, type of time delay with weak kernel and strong kernel as well as the proof of existence of bifurcation. Other problems such as asymptotic stability of bifurcation periodic solution, algorithm of determining the bifurcation direction, asymptotic stability and style of periodic solution are also studied. The average time delay is chosen as the bifurcation parameter, phenomena pertinent to system states of the continuous time delay network with strong kernel evolving from stable to oscillating, then back to stable again are observed.

论文的主要创新之处可以归纳如下： 1）针对目前国内外对神经网络的分岔研究较少的情况，论文中详细讨论了带离散时延神经网络、带弱核的连续时延神经网络、带连续分布时延且具有强核的神经网络的Hopf分岔现象，从理论上证明了Hopf分岔的存在性，并研究了分岔周期解的渐近稳定性，得到了确定周期解的渐近稳定性、分岔方向、周期解的渐近形式的算法；用平均时延作为分岔参数，发现带强核的连续时延神经网络中存在着系统的状态由稳定变化到振荡现象，当继续增加平均时延参数时，又从振荡变为稳定这一特殊的动力学现象。

This note is a revision and complement to [1]. The author proved that alonglower half of the curve C_1 in Fig. 11 of [1] not only m/a→∞ but also a→-∞,and that the upper half of C_1 has its asymptotic direction the straight linem=-2a. Thus, we have revised the original estimation, which stated that theupper half of C_1 has asymptotic direction m=-a.

本文是对文[1]的修正和补充，证明了沿着文[1]图11中的分岐曲线C_1的下半支应当由α→-∞，而它的上半支以直线m=-2α为渐近方向，从而修正了原来对C_1上半支以m=-α为渐近方向的结论，再利用文[1]中原有的知识也就修正了分歧曲线C_2~*的渐近方向。

与asymptotic direction相关的网络解释 [注：此内容来源于网络，仅供参考]

asymptotic direction：渐近方向

asymptotic density 渐近密度 | asymptotic direction 渐近方向 | asymptotic efficiency 渐近效率

asymptotic direction angle：渐近方向角

asymptotic approximation 渐近近似 | asymptotic direction angle 渐近方向角 | asymptotic elastic behavior 渐近弹性行为