The HNN model considered the time delay of signal diffusion and had asymmetric weights.
HNN的连接权是非对称的，并且考虑了信号传播时延。
In this paper, we analyzed the speciality of ga and HNN, and gave an accelerating method.
文章简要分析了GA和HNN算法的特点，并将两种算法有机地相结合，提出了一种加速方法。
Based on the principle of HNN optimization computation, a design method for the observer for linear time invariant systems is proposed.
基于神经优化计算原理，给出了线性定常系统的状态观测器的设计方法。
The safety of the planned path was considered in the weight design of the HNN, and local virtual repulsive forces were formed around obstacles to generate safe path.
HNN权值设计中考虑了路径安全性因素，通过在障碍物附件形成局部虚拟排斥力来形成安全路径。
The second step (recognition) is achieved by using a holographic nearest-neighbor algorithm (HNN), in which vectors obtained in the preprocessing step are used as inputs to it .
第二步，识别阶段，采用了一种亲笔最近相邻算法（HNN）。首先自学习预处理得到的数据，并得到对象的总的特征。再通过HNN算法来识别对象。
The validity of the derived identify scheme is proved by the simulation results of HNN based asynchronous motor drive system parameters' identification in consider of sensors' characteristics.
通过在鼠笼式电机传动系统参数辨识中应用的仿真结果，验证了该辨识方案的正确性。
The validity of the derived identify scheme is proved by the simulation results of HNN based asynchronous motor drive system parameters' identification in consider of sensors' characteristics.
通过在鼠笼式电机传动系统参数辨识中应用的仿真结果，验证了该辨识方案的正确性。
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