Conditions of asymptotic stability for cellular neural networks with time delay

In this paper, global asymptotic stability for cellular neural networks with time delay is discussed using a novel Liapunov function. Some novel sufficient conditions for global asymptotic stability are obtained. Those results are simple and practical than those given by P. P. Civalleri, et al., and have a leading importance to design cellular neural networks with time delay.     

基于强化神经网络的区域协调控制研究

本文在综合神经网络与强化学习的基础上,对智能交通区域协调控制进行了研究。文章首先介绍了基于神经网络存储和训练的强化学习算法,然后在此基础上进行区域协调模型的建立并给出协调的算法,通过仿真实验对模型的有效性进行验证,结果显示本文建立的模型对于试验安徽省合肥市高新区区域内的交通状况的提高有较好的效果。     

基于概率神经网络的农作物虫情预测

为了提高对农作物虫情预测的准确度,本文主要采用概率神经网络（PNN）对农作物虫情进行预测．并将预测结果与BP神经网络的预测结果进行比较。结果表明,概率神经网络的预测准确度远远大于BP神经网络,显示了概率神经网络在模式识别领域的优势。     

改进的BP神经网络在交通流量预测中应用

针对传统BP学习算法收敛速度慢,对步长依赖明显等缺点,提出一种利用搜索较优步长的BP算法.在网络训练中,能够在每次迭代中搜索出一个相对合理的步长,从而使步长的选择对学习速度的影响大大降低.对交通流量预测仿真结果表明,新算法对步长选择的依赖性小于传统BP算法.     

基于六氟化硫气体分解物的高压设备专家诊断系统

本文采用小波神经网络的专家诊断方法,提出了一种基于六氟化硫气体分解物的高压设备专家诊断系统的思路和方法,并且将良好的软件和硬件协同应用于高压设备专家诊断中,用于分析气体分解物的含量并做出高压设备的故障判断与对故障的预测。介绍专家诊断系统的硬件组成和软件实现方式,并通过与其他业内常用方法的比较进而突出本方法的优势。最后,通过分析一些列实验数据证明专家诊断系统的可靠性和实用性。     

Performance of Object Classification Using Zernike Moment

Moments have been used in all sorts of object classification systems based on image. There are lots of moments studied by many researchers in the area of object classification and one of the most preference moments is the Zernike moment. In this paper, the performance of object classification using the Zernike moment has been explored. The classifier based on neural networks has been used in this study. The results indicate the best performance in identifying the aggregate is at 91.4% with a ten orders of the Zernike moment. This encouraging result has shown that the Zernike moment is a suitable moment to be used as a feature of object classification systems.     

Performance Analysis of Layer Pruning on Sphere Decoding in MIMO Systems

Sphere decoding (SD) for multiple‐input and multiple‐output systems is a well‐recognized approach for achieving near‐maximum likelihood performance with reduced complexity. SD is a tree search process, whereby a large number of nodes can be searched in an effort to find an estimation of a transmitted symbol vector. In this paper, a simple and generalized approach called layer pruning is proposed to achieve complexity reduction in SD. Pruning a layer from a search process reduces the total number of nodes in a sphere search. The symbols corresponding to the pruned layer are obtained by adopting a QRM‐MLD receiver. Simulation results show that the proposed method reduces the number of nodes to be searched for decoding the transmitted symbols by maintaining negligible performance loss. The proposed technique reduces the complexity by 35% to 42% in the low and medium signal‐to‐noise ratio regime. To demonstrate the potential of our method, we compare the results with another well‐known method — namely, probabilistic tree pruning SD.     

基于Welch功率谱和卷积神经网络的通信辐射源个体识别

针对低信噪比条件下通信辐射源个体识别率低的问题,提出了一种基于Welch功率谱和卷积神经网络的通信辐射源个体识别方法.构建了由20个基于ZigBee协议的物联网设备组成的测试平台,将ZigBee信号前同步码部分的Welch功率谱数据作为辐射源指纹特征送入卷积神经网络进行分类.该方法在低信噪比条件下很好地保留了辐射源的指纹特征,结合卷积神经网络强大的微特征提取能力,对辐射源进行了有效分类.实验结果证明,在瑞利信道及低信噪比条件下,所提方法的识别效果明显优于其他方法.     

面向肺结节多语义特征分类的不确定性多任务损失方法

肺结节的早期诊断对后续的治疗非常重要.尽管深度学习方法在肺结节良恶性分类等任务中取得了良好的结果,但是这些方法没有提供有意义的诊断功能,导致获得的结果缺乏客观性.越来越多的研究者引入了肺结节的其他语义特征来解决这个问题,但是多个语义特征的引入会造成模型的负迁移.为了解决肺结节多个语义特征之间同步共享知识的程度不同造成的...     

基于TW3-C RUS法的骨龄评估方法研究

经典TW3-C RUS (Tanner and Whitehouse 3-Chinese RUS)法将手骨的关键骨骺区域严格划分为9个等级,未充分考虑骨骺发育的连续性,导致骨龄评估存在一定误差。针对该问题,本文提出一种基于TW3-C RUS法的改进骨龄评估方法。采用阈值法的思想,动态选择网络模型输出的前N个等级概率值,并将前N个概率值作为权值计算手骨的加权得分,降低由于手骨单一等级判定引起的误差。针对网络模型冗余问题,采用跨阶段局部网络(cross stage partial network, CSP-Net)轻量化深度残差网络(residual network 50,Resnext50)。实验表明,改进后的方法对男性骨龄评估的平均绝对误差(mean absolute error,MAE)为0.421 4岁,女性MAE为0.412 8岁,相比于经典TW3-C RUS法,骨龄评估准确率有明显提升。轻量化后的网络模型参数量为46.28 MB,相比Resnext50网络模型有明显降低。