本文讨论了一类脉冲神经网络的周期解的指数稳定性。
This paper discusses the exponential stability of periodic solution for impulsive neural networks.
在软件中提供了多种反演方法如基于模型反演，稀疏脉冲反演和人工智能神经网络反演。
Multi inversion methods are provided in the software, such as inversion based on models, sparse pulses inversion and artificial intelligence neural network inversion.
提出了一种双层脉冲耦合神经网络与数学形态学相结合的区域标识算法。
This paper brings forward a new approach for region labeling by using double Pulse Coupled Neural Networks (PCNN) and morphology.
建立了强脉冲电磁场作用下铝合金凝固组织晶粒尺寸的人工神经网络BP算法模型。
BP algorithmic model was established for the artificial neural network of the grain size of Al-alloy's solidification structure under the action of strong pulse electromagnetic field.
将上述方法用于实际脉冲TIG焊过程控制，结果表明模糊神经网络控制方案有效。
The results of experiment on the pulse TIG welding process show that the fuzzy inference-neural network control scheme presented in this paper is effective.
针对同一场景多聚焦图像的融合问题，提出了一种基于小波变换的自适应脉冲耦合神经网络(PCNN)图像融合方法。
Concerning the fusion problem of multi-focus image with the same scene, an algorithm of image fusion based on wavelet transform and adaptive Pulse Coupled Neural Network (PCNN) was proposed.
但是对于具有脉冲的细胞神经网络所具有动力学性质，现阶段所得的成果还比较少。
However, the results about the dynamic properties of cellular neural network with impulse are still relatively rare.
提出了一种脉冲耦合神经网络和最大相关准则相结合的算法来对图像进行分割。
The algorithm that segments images with pulse-coupled neural networks(PCNN) and two-dimension maximum correlation criterion(MCC) was presented.
提出了一种用于脉冲电镀电源的神经网络控制器，并对脉冲变压器建立了数学模型。
A control method based on neural network for pulse plating power is presented, and a mathematical model of the pulse transformer is established.
同时现实生活也给细胞神经网络提出了更多的问题，比如脉冲现象。
At the same time, the real world also ask cellular neural network to solve more and more problems, such as impulsive phenomenon.
论述了用神经网络中的前向网络BP算法来计算脉冲开关角的一种计算方法，并进行了MATLAB仿真。
This paper discusses about an algorithm of calculating the pulse switch Angle by using of forward network BP algorithm based on the Neural network, and makes the MATLAB simulation.
详细地讨论了增益、学习速率、动量等网络参数对神经网络收敛速度和导数脉冲伏安法计算结果的影响。
The effects of neural network parameters including gain, learning rate, and momentum on network convergence and DPV computation results have been investigated.
详细地讨论了增益、学习速率、动量等网络参数对神经网络收敛速度和导数脉冲伏安法计算结果的影响。
The effects of neural network parameters including gain, learning rate, and momentum on network convergence and DPV computation results have been investigated.
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