神经网络与非线性模式数据挖掘研究

论述了人工智能技术在数据挖掘领域应用的一些理论进展。非线性模式的规则提取是数据挖掘的一个主要任务,然而,目前有效的方法却很少。着重论述了一个专用于对非线性模式数据进行数据挖掘的模型,并且给出了简要的算法和一个例子。     

基于WiFi信道状态信息的人员活动持续时间估计

针对传统人员活动持续时间估计系统隐私性及灵活性较差的问题，分析信道状态信息（CSI）的幅度变化，提出了一个基于WiFi信道状态信息的人员活动持续时间估计系统。该系统重点将连续复杂的人员活动持续时间估计问题转化为离散简单的人员检测问题。首先，采集CSI信息并滤除异常值和噪声；其次，利用主成分分析法（PCA）进行子载波降维，获取主成分以及相应的特征向量；随后计算主成分方差和特征向量一阶差分均值，并将两者比值作为特征值训练反向传输神经网络（BPNN）模型；然后，利用训练好的BPNN模型进行人员检测，并当检测出有人员活动时，进一步对CSI数据进行等宽分割；最后，针对所有分割后的CSI数据实现人员检测，并依据符合人员检测结果的数据来估计人员活动的持续时间。在真实室内环境中对系统进行实验评估，人员检测平均准确率可达到97%，活动持续时间误差不超过10%。实验结果表明，该系统能够有效估计出人员活动的持续时间。     

电子舌在黄酒检测中的应用

运用Astree型电子舌对不同的黄酒样品进行了检测,并结合PCA、DFA及BP神经网络,对3个厂家不同酒龄的9个样品进行分析。结果表明,电子舌不仅可以区分不同厂家的产品,同时也可以很好地区分同一厂家不同陈酿时间的黄酒样品。对古越龙山品牌3个酒龄各8个批次的样品进行了理化指标的分析,并将数据与电子舌传感器数据信息建立BP神经网络模型,结果显示除氨态氮含量的拟合与预测较为准确外,总酸、pH值等其他理化指标的拟合与预测效果都不理想。     

基于PSO-BPNN模型的氯氧镁水泥混凝土耐水性预测

为快速准确地获得具有优异耐水性氯氧镁水泥混凝土（MOCC）的配合比,设计了拓扑结构为4-10-2的粒子群优化（PSO）算法-反向传播（BP）神经网络（PSO-BPNN）模型.该模型的输入层参数为n（MgO）/n（MgCl₂）、粉煤灰掺量、磷酸掺量和磷肥掺量,输出层参数为MOCC的抗压强度和软化系数;模型数据集为144组,其中训练集数据为100组,验证集数据为22组,测试集数据为22组.结果表明：PSO-BPNN模型在MOCC抗压强度预测中的评价参数——决定系数R²=0.99、平均绝对误差S_MAE=0.52、平均绝对误差百分比S_MAPE=1.11、均方根误差S_RMSE=0.73;其在软化系数预测中的评价参数——R²=0.99、S_MAE=0.44、S_MAPE=1.29、S_RMSE=0.62;与BP神经网络（BPNN）模型相比,PSO-BPNN模型具有更强的双参数预测能力,可用于MOCC配合比的正向设计和反向指导.     

Signal analysis‐based fault classification and estimation of fault location of an unbalanced network using S‐transform and neural network

This paper demonstrates a technique for the diagnosis of the type of fault and the faulty phase on an overhead transmission line, followed by locating the particular fault on the affected phase. The power system network considered in this study is a three‐phase transmission line with unbalanced loading simulated in the PowerSim Toolbox of MATLAB. S‐transform is used to compute the energy components of the voltage signals of the three phases of the transmission line. These features are used as input vectors of a probabilistic neural network (PNN) for fault detection and classification. Detection of the faulty phase(s) is followed by estimation of fault location. The voltage signal of the affected phase is processed to generate the S‐matrix. The frequency components of the S‐matrices for different fault locations are used as input vectors for training a backpropagation neural network (BPNN). The results are obtained with satisfactory accuracy and speed. All the simulations have been done in MATLAB environment for different values of fault locations, fault resistances, and fault inception angles. The effect of noise on the simulated voltage signals has been investigated. The analysis has been further extended by implementing the proposed method in a modified version of IEEJ West 10 machine system model. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

注塑模具成本估算系统的设计及实现

采用工时法、BP神经网络法和CBR方法开发了一个注塑模具的成本估算系统,为快速准确地进行模具报价提供了一套有效可行的方法.介绍了系统总体设计和系统CBR方法实现中所用到的一些关键技术,并通过一个实例验证了此系统的有效性.     

一类大规模系统神经网络自适应控制方案设计

针对一类大规模非线性机械系统,如自动化高速公路汽车空位调节和连续搅拌反应釜等系统,设计了一套基于前向神经网络的分散自适应控制方案以实现对其有效控制．作为直接自适应控制器,神经网络被用于逼近未知函数,所设计的两个鲁棒控制项分别用于消除互连效应和干扰项．系统稳定性得到严格证明,通过仿真进一步验证了方案的有效性．     

Process parameter optimization for ionic liquids as gas separation membranes based on a GWO–BPNN model

Gas separation membranes offer a cost-effective solution for capturing greenhouse gases, mitigating the global greenhouse effect. Ionic liquids (ILs) have emerged as one of the promising materials for greenhouse gas separation due to their strong affinity for CO₂. In this study, we propose a laboratory-scale method for preparing IL–PVDF blend membranes with high CO₂/N₂ selectivity. The separation performance of the membranes was evaluated using a custom gas permeation measurement system. The effects of casting solution composition, solidification method, and film-forming processes on separation performance were experimental investigated, and the obtained experimental data were used to train a back propagation neural network (BPNN) optimized by the Gray Wolf Optimizer (GWO) algorithm. This hybrid GWO–BPNN model was utilized to predict separation membrane efficiency, optimize the film-forming process, and identify the optimal range of process parameters. Notably, the GWO–BPNN model demonstrated a 2.76% higher prediction accuracy compared to a standalone BPNN. The results indicated that the GWO–BPNN algorithm has a great potential to accurately predict membrane separation efficiency and apply in optimal membrane process design (OMPD), and this method can significantly reduce the number of experimental trials required to achieve OMPD.     

变压器色谱监测中的BPNN故障诊断法

本文将ＢＰ神经网络应用于变压器故障诊断。通过验证，结果显示ＢＰＮＮ诊断法有良好的应用前景。     

Greyscale based learning in BPNN for image restoration problem

A new method of back propagation learning with respect to the problem of image restoration which is named as greyscale based learning in back propagation neural networks ( BPNN) is investigated. It is observed that by using this method the value of mean square error ( MSE) decreases significantly. In addition,this method also gives good visual results when it is applied in image restoration problem. This method is also useful to tackle the inherited drawback of falling into local minima by reducing its effect on overall system by bifurcating the learning locally different for different grey scale values. The performance of this algorithm has been studied in detail with different combinations of weights. In short,this algorithm provides much better results especially when compared with the simple back propagation algorithm with any further enhancements and without going for hybrid solutions.