Global exponential stability for interval general bidirectional associative memory (BAM) neural networks with proportional delays

This paper is concerned with interval general bidirectional associative memory (BAM) neural networks with proportional delays. Using appropriate nonlinear variable transformations, the interval general BAM neural networks with proportional delays can be equivalently transformed into the interval general BAM neural networks with constant delays. The sufficient condition for the existence and uniqueness of equilibrium point of the model is established by applying Brouwer's fixed point theorem. By constructing suitable delay differential inequalities, some sufficient conditions for the global exponential stability of the model are obtained. Two examples are given to illustrate the effectiveness of the obtained results. This paper ends with a brief conclusion. Copyright © 2016 John Wiley & Sons, Ltd.     

Bayesian network inference using marginal trees

Variable elimination (VE) and join tree propagation (JTP) are two alternatives to inference in Bayesian networks (BNs). VE, which can be viewed as one-way propagation in a join tree, answers each query against the BN meaning that computation can be repeated. On the other hand, answering a single query with JTP involves two-way propagation, of which some computation may remain unused. In this paper, we propose marginal tree inference (MTI) as a new approach to exact inference in discrete BNs. MTI seeks to avoid recomputation, while at the same time ensuring that no constructed probability information remains unused. Thereby, MTI stakes out middle ground between VE and JTP. The usefulness of MTI is demonstrated in multiple probabilistic reasoning sessions.     

结合NSDTCT和稀疏表示的遥感图像融合

为了提高全色图像与多光谱图像的融合质量,提出一种基于非下采样双树复轮廓波变换和稀疏表示的图像融合算法.对多光谱图像进行亮度-色度-饱和度变换,再对亮度分量和全色图像进行直方图匹配及亮度平滑滤波处理.利用非下采样双树复轮廓波变换处理亮度分量和全色图像,得到对应的高低频系数.对于低频系数,利用稀疏表示进行融合,采用空间频率和l1范数双指标取大的融合规则得到稀疏表示系数;对于高频系数,将改进的拉普拉斯能量和作为脉冲耦合神经网络的外部输入项,提出了改进的脉冲耦合神经网络的融合策略.最后进行非下采样双树复轮廓波逆变换和亮度-色度-饱和度逆变换得到融合结果.实验结果表明,该算法最大限度地保留光谱信息的同时可以提高空间分辨率,视觉效果及客观指标均优于经典的融合算法.     

基于太赫兹时域光谱技术与PCA-BPN网络的转基因大豆鉴别

基于太赫兹波段内的光谱分析技术以及主成分特性分析与反向前馈神经网络建模,提出了一种转基因大豆鉴别方法.从光谱数据中提取累计方差贡献率达到97.582％的前8种主成分因子,并将其作为输入源导入神经网络模型,通过剔除冗余数据、降低数据维数,所建立的神经网络模型能准确识别校验集.该方法可以实现转基因大豆的快速、无损检测,在农业安全领域有广泛的应用前景.     

基于改进蚁群算法的LEACH协议研究

针对LEACH协议在数据传输阶段,簇首与汇聚节点之间采用单跳模式传输数据使得能量消耗快并且不均衡的问题,提出一种基于改进蚁群算法的新型路由协议。该协议利用了能耗因子对蚁群转移概率以及信息素更新进行改进,充分考虑了节点的剩余能量和节点间距离,通过信息素的建立和更新,寻找簇首节点和基站之间的最优传输路径,进行多跳传输模式,从而均衡簇首节点能量消耗。仿真实验结果表明,改进后的ACO-BEC协议较之于LEACH协议,能够有效降低了整个网络能量消耗,延长了网络寿命。     

基于多维状态参量和时间序列对电机的异常检测

针对传统对电机的异常检测常常出现误判或滞后的情况,采用基于时间序列对电机单一状态参量用AR拟合,同时利用SOM神经网络无监督的方式量化电机数据。然后,利用得到的量化序列结合AR曲线得到序列的转移概率,及早发现某种状态参量的异常变化;之后,DBSCAN算法挖掘多维参数之间特征关系来确定电机是否出现异常。最后结合实例说明该方法的检测过程,并对比验证了该方法的优越性。     

基于神经网络和HVS的空域数字水印算法

为了弥补空间域水印算法在提取水印时需要原宿主图像、鲁棒性较差等缺点,提出一种基于BP神经网络和人眼视觉特性(HVS)的空域数字水印算法。先选出原宿主图像对比度函数值最大的前块用于二值水印图像的嵌入,利用神经网络能够逼近任意非线性关系的特点和其自适应性,构建水印信号嵌入前后图像块像素值间的映射关系,实现了水印的盲提取,并将该算法与现有算法进行了对比。实验结果表明,对于JPEG压缩、加噪、剪切和旋转等常见的图像处理攻击,算法具有良好的鲁棒性和不可见性。     

基于NW型小世界人工神经网络的污水出水水质预测

为了预测污水处理出水水质,针对污水处理过程具有多变量、非线性、时变性、严重滞后的特点,提出了基于NW型小世界人工神经网络的污水处理出水水质预测模型。首先根据污水处理系统确定模型输入输出变量个数,然后建立了多层前向小世界神经网络模型,并对网络模型的隐层结构进行了优化研究。借助污水处理过程的历史数据进行了仿真研究,结果表明：和同规模的多层前向人工神经网络相比,小世界神经网络对污水出水水质预测具有较高精度和收敛速度,为污水出水水质的实时预测提供了一种有效的新方法。     

异构网络动态配置空白子帧干扰协调算法

为了解决LTE-A系统下两层异构网络中的小区间干扰协调问题,提出了在增强小区中调度几乎空白子帧的干扰方案。为了降低干扰和进一步提高信道利用率,设计了一种动态分配几乎空白子帧的算法。该算法基于小区增强覆盖技术,指出由增强小区用户与总用户的比例来动态的调整几乎空白子帧比例。仿真结果对比了固定比例的几乎空白子帧和动态设定几乎空白子帧的算法,表明动态配置几乎空白子帧的算法,提高了系统容量,改善了信道利用率。     

Trace determination of safranin O dye using ultrasound assisted dispersive solid-phase micro extraction: Artificial neural network-genetic algorithm and response surface methodology

In this study, ultrasound assisted dispersive solid-phase micro extraction combined with spectrophotometry (USA-DSPME-UV) method based on activated carbon modified with Fe₂O₃ nanoparticles (Fe₂O₃-NPs-AC) was developed for pre-concentration and determination of safranin O (SO). It is known that the efficiency of USA-DSPME-UV method may be affected by pH, amount of adsorbent, ultrasound time and eluent volume and the extent and magnitude of their contribution on response (in term of main and interaction part) was studied by using central composite design (CCD) and artificial neural network-genetic algorithms (ANN-GA). Accordingly by adjustment of experimental conditions suggested by ANN-GA at pH 6.5, 1.1 mg of adsorbent, 10 min ultrasound and 150 μL of eluent volume led to achievement of best operation performance like low LOD (6.3 ng mL⁻¹) and LOQ (17.5 ng mL⁻¹) in the range of 25–3500 ng mL⁻¹. In following stage, the SO content in real water and wastewater samples with recoveries between 93.27–99.41% with RSD lower than 3% was successfully determined.