1) Temporary Chaos
短暂混沌
2) transient chaos
暂态混沌
1.
Two neuron system with an inertial term to exhibit transient chaos;
有惯性项的二元神经网络系统存在暂态混沌
2.
Based on a neural network with transient chaos and time-variance gain,neural network structure and computational energy function for solving optimal route selection was constructed,and then an algorithm which can support the driver in deciding on an optimal route to his preference was proposed.
采用广义路阻的定义,考虑了驾驶员在路径选择中的不同要求,并借助一种具有暂态混沌和时变增益的神经网络(NNTCTG),针对最优路径选择问题设计了神经网络结构,构造了能量函数,提出了一种能够满足不同出行者偏好的最优路径选择算法。
3.
By introducing transient chaos and time variant gain, the proposed chaotic neural network has richer and more flexible dynamics than Hopfield like neural networks only with point attractors, so that it can be expected to have higher ability of searching for globally optimal or near optimal solutions.
通过引入暂态混沌和时变增益，该网络比Ｈｏｐｆｉｅｌｄ型网络具有更加丰富和更为灵活的动力学特性，从而具有更强的搜索全局最优解或近似全局最优解的能力。
3) chaotic transient
暂态混沌
1.
This paper studied the chaotic transient phenomena of a controlled electromechanical system with a time delay in the feedback path.
研究了一种具有时滞反馈的磁悬浮轴承系统的暂态混沌现象。
2.
It was shown that chaotic transients occurred in the system with tw.
本文进一步考查了Qi四维系统,借助于相图和时间历程曲线图等手段,发现该系统还可能发生暂态混沌现象,初始条件的微小改变可使系统的最终稳态由一种改变为另一种。
3.
This paper presents a study on the chaotic transient phenomena of a controlled electromechanical system with a time delay in the feedback path.
本文研究一种具有时滞反馈的磁悬浮轴承系统的暂态混沌现象。
4) Transient chaotic dynamics
暂态混沌动力学
5) transiently chaotic neural network
暂态混沌神经网络
1.
A algorithm based on transiently chaotic neural network is proposed to solve the protein contact maps problem.
针对蛋白质关联图预测问题,提出一种暂态混沌神经网络实现方法,所提出的方法具有暂态混沌特性和平稳收敛特性,能有效避免传统Hopfield神经网络极易陷入局部极值的缺陷。
6) transiently chaotic network
暂态混沌神经网络
1.
This paper analyzed that the dynamic characteristics of transiently chaotic networks(TCNN) quite sensitively depend on value of the self-feedback connection weights,and researched the annealing function that intensively influences the veracity and search speed of TCNN module.
分析了暂态混沌神经网络(TCNN)模型的动力学特性对自反馈连接权值的敏感性,研究了退火函数对优化过程中的准确性和计算速度的影响。
2.
This paper analyses that the dynamic characteristics of transiently chaotic networks (TCNN) quite sensitively depend on value of the self-feedback connection weights, and researches the annealing function that intensively influences the veracity and search speed of TCNN module.
本文分析了暂态混沌神经网络(TCNN)模型的动力学特性对自反馈连接权值的敏感性, 研究了退火函数对优化过程中的准确性和计算速度的影响。
补充资料：短暂
（时间）短：经过～的休息，队伍又开拔了ㄧ我跟他只有过～的接触。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条