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neural相关的网络例句

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与 neural 相关的网络例句 [注:此内容来源于网络,仅供参考]

Dux and Marois are the first to identify the regions of the brain responsible for this multitasking bottleneck, by examining patterns of neural activity over time.

dux和marois是首先确定地区的大脑负责这项工作的多瓶颈,通过审查模式的神经活动的时间。

Dux and Marois are the first to identify the regions of the brain responsible for this bottleneck, by examining patterns of neural actiity oer time.

Dux和Marois通过长期的中枢活动模式研究,首次证实大脑的某些区域起到了瓶颈的作用。

The dyn amic characteristic model is bui lt and the simulation is made by using GA based neural network.

用基于遗传算法的神经网络对滑觉传感器的动态性能进行了建摸和仿真,从传感器应用的角度出发,依据"逆模型"思想,采用神经网络对传感器建立辨识模型,从而提高了机械手的控制精度和灵敏度

The nonlinear mapping of artificial neural network was used to construct a complicated mathematical model in terms of dynamic friction factor and influencing parameters.

同时编制程序进行多参数共同作用下的摩擦因数实例仿真分析,结果表明研究方法的可行性。

For the problem of parameter variation and nonlinear dynamic friction compensation, a recurrent-neural-network-based adaptive- backstepping control for servo system was proposed.

针对伺服系统的系统参数摄动和非线性动态摩擦补偿问题,提出基于递归神经网络的自适应反步控制系统设计方法。

Simulation results demonstrate that LS-SVM has a good approximate capability for inverse model, and the proposed compound control system has better dynamic track performance, resistance to disturbance of system and robustness than RBF neural network.

仿真研究结果表明LS-SVM的逆模型辨识能力强,该复合控制系统具有比基于最近邻聚类的RBF神经网络逆控制系统更优的动态跟踪性能,更好的抗干扰能力和鲁棒性。

Artificial neural network is a kind of nonlinear dynamic system that consists of large numbers of simple processing units.

人工神经网络是由大的简单处理单元所构成的非线性动力学系统。

Stochastic variables included driven accelerations, driven time, torques, frictions and damps were considered basically. In the first, Monte Carlo method was applied to generate stochastic variables and dynamical responds of mechanism. Secondly, the application of Artificial Neural Network was motivated by the approximate concepts inherent in reliability analysis and time consuming repeated analyses required for MC.

将驱动加速度、驱动时间、摩擦和阻尼力等作为随机变量,应用蒙特卡罗方法,取得动态参数样本,再利用人工神经网络方法,根据抽取的样本对网络进行训练,统计网络输出得到动态应力分布,进而求出机构动态强度可靠度。

Chapter 4 is intended to study the dynamical properties of a class of discrete neural networks.

本文的第四章则主要是关于离散神经网络的稳定性质研究。

A survey is presented on dynamical behavior of neural networks, as well as the methods of research.

对神经网络系统动力学行为研究的国内外概况和研究方法进行了概括。

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