脉冲神经网络

然而，在面向刺激编码的前向网络模型中，抑制性反馈对神经元相关性放电活动的影响还不甚明确。面向刺激编码的反馈脉冲神经网络的相关性放电机制亟待深入研究。

Whereas , the effect of inhibitory feedback on correlated activity in network models for stimulus coding is poorly understood and the mechanisms underlying correlated firing in feedback spiking neural networks for stimulus coding need further study .

提出了基于Gabor小波变换和脉冲神经网络（PCNN）的掌纹识别算法。

We propose a palmprint recognition based on Gabor features and Pulse Coupled Neural network ( PCNN ) .

利用Lyapunov函数并结合不等式的技巧，给出了一些充分判据来确保一类脉冲神经网络系统具有全局指数稳定性的周期解。

By the Lyapunov function method , combined with the inequality techniques , some criteria are established to ensure the existence , uniqueness and global exponential stability of the periodic solution for a class of impulsive neural networks .

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 .

这些充分判据在实际应用中非常容易验证，也可利用这些判据来设计全局指数周期的脉冲神经网络。

The conditions are easy to check in practice and they can be applied to design globally exponentially periodic impulsive neural networks .

脉冲神经网络是目前拟人性能较强的一种新型网络，通过模拟树突和轴突之间产生的脉冲处理各种信息。

Spiking neural network is the personification of strong performance of new type of network , generating the spikes through simulation between the dendrites and the axons processing all kinds of information .

本文通过在脉冲神经网络中对输入信号加入一定数值范围内的随机扰动来研究其分类能力的鲁棒性。

In this article , we study random perturbations within the range of values of the input signal to the spiking neural network is used to study the robustness of the classification ability .

在面向刺激编码的反馈脉冲神经网络中，采用功率谱密度描述了兴奋性神经元在毫秒时间尺度上的相关性放电活动，证实了网络可以产生伽马频域的同步振荡。

The correlation of the excitatory neurons in millisecond time scale is described by power spectral density . The feedback spiking neural network for stimulus coding is proved to oscillate in gamma frequency .

而脉冲神经网络对于输入信号的敏感性特别强，所以输入信号受到干扰，脉冲神经网络的处理能力很容易受噪声的影响而产生一定的扰动。

Spiking neural network is particularly strong for the sensitivity of the input signal , the input signal is disturbed , the processing power of the spiking neural network is vulnerable to the impact of disturbance .

更为有趣的一个结果是网络受到扰动后的识别率反而有所提高，这恰好从另一个方面佐证了脉冲神经网络的较强的拟人性。

But a more interesting result is that the recognition rate of the disturbed network is improved . This is just another proof which illustrates the strong personification of the spiking neural network of the abstract .

脉冲耦合神经网络（PulseCoupledNeuralNetwork，PCNN）具有良好的脉冲传播特性，在图像分割中得到了广泛应用。

For its good property of pulse burst , Pulse Coupled Neural Network is widely used in image segmentation .

提出了一种基于脉冲耦合神经网络（Pulse-CoupledNeuralNetwork，PCNN）的最短路径算法。

Proposed shortest-path algorithm using slightly modified Pulse-Coupled Neural Network .

该文首次提出了一种将有生物视觉依据的人工神经网络&脉冲耦合神经网络（PulseCoupledNeuralNetwork，以下简称为PCNN）用于说话人识别领域的语谱图特征提取的新方法。

This paper proposes an innovative method of feature extraction of Spectrogram using Pulse Coupled Neural Network ( PCNN ) in speaker recognition for the first time .

脉冲耦合神经网络（PulseCoupledNeuralNetwork，PCNN）是根据对动物的大脑视觉皮层同步脉冲发放所获得的实验结果。

The Pulse Coupled Neural Network ( PCNN ) was experiment result which obtained to explain the synchronous burst of the neurons in the animals ' visual cortex .

脉冲耦合神经网络（PulseCoupledNeuralNetwork，PCNN）可有效的用于图像分割，但无法确定最优分割时的迭代次数。

Pulse coupled neural network ( PCNN ) can be implemented on image segmentation effectively . However , the cyclic iterative times of optimal segementation can not be determined .

另一方面将反馈脉冲耦合神经网络与滑降分割相结合，提出了一种新的MRI图像分割的特征提取算法。

Secondly , combining the feedback pulse coupling neural network ( FPCNN ) with toboggan segmentation algorithm , this thesis presents a new MRI image segmentation feature extraction methods .

脉冲耦合神经网络（PulseCoupledNeuralNetwork，PCNN）新一代人工神经网络网络，它具有良好的生物学特性，与传统的人工神经网络有很大区别。

Pulse coupled neural network ( PCNN ) is a new generation artificial neural network which has a better biological characteristics and is different from the traditional artificial neural network .

脉冲耦合神经网络（PulseCoupledNeuralNetwork，简称PCNN）作为第三代人工神经网络，比传统人工神经网络更好地模仿了生物神经网络，尤其适合图像处理，是目前的研究热点和重点。

As the third generation neural network , Pulse Coupled Neural Network ( PCNN ), which especially suits image processing , imitates biology neural network better compared with traditional neural network .

脉冲耦合神经网络（PCNN）非常适合图像分割。

Pulse-Coupled Neural Networks ( PCNN ) is very suitable for image segmentation .

基于直方图的脉冲耦合神经网络（PCNN）多门限图像分割方法

Pulse coupled neural networks based on histogram for image mulu - threshold segmentation

论文在以下方面进行了创新性工作：1、在深入研究脉冲耦合神经网络模型（PulseCoupledNeuralNetwork，PCNN）的基础上，给出了脉冲耦合神经网络模型参数确定的准则。

The paper contributes to the following creative work in the following aspects : 1 . Based on further study in the Pulse Coupled Neural Network ( PCNN ), the rule of parameters determination is proposed .

研究了脉冲Hopfield神经网络在Hopfield意义下的鲁棒稳定性。

This paper studies the robust H stability ( e.g. in the sense of Hopfield ) for Hopfield neural networks ( HNN for short ) with impulsive effects .

本文在分析脉冲耦合神经网络（Pulse-CoupledNeuralNetworks，以下简称PCNN）工作机理的基础上，着重研究了它在图像滤波和图像边缘检测中的应用。

This thesis firstly gives a detailed analysis of the mechanism of Pulse-coupled Neural Networks ( Simplified as PCNN ), and then emphasizes its applications on image noise removal and edge detection .

基于脉冲耦合神经网络（PCNN）的基本原理，提出了一种新型的图像融合算法。

Based on the principle of pulse-coupled neural network ( PCNN ), a novel algorithm for image fusion is pre-sented .

该文提出了如何用脉冲耦合神经网络（PCNN）进行图像阴影处理的新方法。

This paper brings forward a new approach for image shadow processing based on PCNN ( Pulse Coupled Neural Network ) .

本文提出了一种基于简化脉冲耦合神经网络（PCNN）模型的不规则分割区域压缩编码方法。

A irregular segmented region coding algorithm based on Pulse Coupled Neural Network ( PCNN ) is presented in this paper .

深入分析了数字图像分割的理论、方法以及脉冲耦合神经网络（PCNN）在数字图像处理方面的应用。

And give a brief introduction to the application of pulse coupled neuron networks ( PCNN ) in digital image processing .

脉冲耦合神经网络（PulseCoupledNeuralNetwork，简称PCNN）是一种单层、无需训练的新型神经网络，它模拟了哺乳动物视觉皮层系统的同步脉冲振荡现象，是第三代神经网络。

Pulse Coupled Neural Network ( PCNN ) is a novel neural network with single layer and no training . It emulates the synchronous pulse oscillation phenomenon found in mammals ' visual cortex system . It is called the third generation neural network .

提出了如何用一种有生物视觉依据的人工神经网络&脉冲耦合神经网络（PulseCoupledNeuralNetwork，PCNN）进行二值文字空洞滤波，即将二值文字中的空洞填满，从而便于后续的文字识别。

This paper brings forward how to do binary character hole-filter , namely fill holes of binary characters by using PCNN ( Pulse Coupled Neural Network ), which has the biologically visual background , so that the next character recognition can be realized .

脉冲耦合神经网络（PCNN）是20世纪90年代形成和发展的一种新型神经网络。

Pulse Coupled Neural Networks ( PCNN ) is a new Neural Networks , which was developed and formed in the 1990 ' s.