DAMAGE CLASSIFICATION BY PROBABILISTIC NEURAL NETWORKS BASED ON LATENT COMPONENTS FOR TIME-VARYING SYSTEM

A new approach to damage classification for health monitoring of a time-varylng system is presented. The functional-series time-dependent auto regressive moving average （FS-TARMA） time series model is applied to the vibration signal observed in the time-varying system for estimating the TAR/TMA parameters and the innovation variance. These parameters are the functions of the time, represented by a group of projection coefficients on the certain functional subspace with specific basis functions. The estimated TAR/TMA parameters and the innovation variance are further used to calculate the latent components （LCs） as the more informative data for health monitoring evaluation, based on an eigenvalue decomposition technique. LCs are then combined and reduced to numerical values （NVs） as feature sets, which are input to a probabilistic neural network （PNN） for the damage classification. For the evaluation of the proposed method, numerical simulations of the damage classification for a tlme-varylng system are used, in which different classes of damage are modeled by the mass or stiffness reductions. It is demonstrated that the method can identify the damages in the course of operation and the change of parameters on the time-varying background of the system.

Computer vision-based limestone rock-type classification using probabilistic neural network

Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.

Remote Sensing Image Segmentation with Probabilistic Neural Networks

This paper focuses on the image segmentation with probabilistic neural networks (PNNs). Back propagation neural networks (BpNNs) and multi perceptron neural networks (MLPs) are also considered in this study. Especially, this paper investigates the implementation of PNNs in image segmentation and optimal processing of image segmentation with a PNN. The comparison between image segmentations with PNNs and with other neural networks is given. The experimental results show that PNNs can be successfully applied to image segmentation for good results.

EEG classification based on probabilistic neural network with supervised learning in brain computer interface

Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

An Advanced Probabilistic Neural Network for the Design of Breakwater Armor Blocks

In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables. The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of＇ van der Meet, and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network （ANN） model. The APNN shows better results in predicting the stability number of armor bilks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.

Passenger Flow Status Evaluation in Subway Station Based on Probabilistic Neural Network

This paper select the escalator with large flow in the station as the object, analysing the correlation of the AFC data of the in and out gates and the passenger flow parameters by passenger flow density and the passing time acquired and calculated in the waiting area of the prediction escalator to select the gates related to the predicted the escalator. NARX neural network is used to predict the model of the passenger flow parameters of the escalator waiting area based on the related gates＇ AFC data, then a probabilistic neural network model was established by using the AFC data and predicted passenger flow parameters as input and the passenger flow status in the escalator waiting area of subway station as output.The result shows the predicting model can predict the passenger flow status of the escalator waiting area better by the AFC data in the subway station. Research result can provide decision basis for the operation management of the subway station.

CPSO优化PNN的陀螺故障诊断方法

针对惯性导航系统中的陀螺仪输出信号非线性、故障特征不明显的问题,为提高惯导系统中惯性器件的故障诊断正确率,提出一种基于改进粒子群算法(PSO)优化概率神经网络(PNN)的陀螺信号故障诊断方法。首先,针对光纤陀螺运行过程中常见的四种故障信号,建立数学模型并进行小波变换提取其故障特征系数;其次,使用Cubic混沌映射以及非线性递减的惯性权重系数对粒子群进行粒子更新,并用于概率神经网络的最优平滑因子选择;最后,训练概率神经网络对陀螺仪故障信号进行分类和诊断。离线测试结果表明,CPSO算法优化的PNN网络针对四种故障分类的平均正确率达到95.8%。

基于LSTM-PNN神经网络的电潜泵故障诊断方法

针对电潜螺杆泵故障预测中发生故障难以及时发现、发现难以准确判别故障类型等问题,提出一种基于深度学习长短期记忆网络(LSTM)结合概率神经网络(PNN)的电潜螺杆泵故障预测方法。以LSTM网络为回归模型,使用时间序列法预测故障信号的未来趋势,利用小波包分解螺杆泵的故障信号,提取其中的故障特征,再结合油压、产量等多个工作参数,构建电潜螺杆泵的故障特征向量,并凭借PNN网络判别预测信号故障类型。收集新疆油田120组故障数据作为数据集对预测模型进行训练,从中取出90组数据作为故障数据库对模型进行训练,取出30组数据作为测试组测试模型准确率,使用LSTM-PNN神经网络预测模型分别对两组数据进行电潜螺杆泵故障预测。结果表明:预测前提取故障信号特征可有效提高电潜螺杆泵的故障预测精度,较常规电潜螺杆泵故障预测方法,LSTM-PNN网络预测具有更高的准确率且准确率提升了3%~16%。

基于BA-PNN算法与数字孪生的车间扰动判定方法

随着科学技术的发展,生产安全和车间管理问题越来越受到重视。传统车间在管理上多依靠人工,使得车间扰动事件发现不及时,扰动认定不清楚,不利于迅速解决扰动事件和保障人员设备安全。为提高管理效率和保障安全,提出一种基于蝙蝠算法优化的概率神经网络(Bat Algorithm-Probabilistic Neural Network,BA-PNN)算法和数字孪生的车间扰动判定方法。首先通过传感器采集数据并对其进行分析和预处理;随后搭建传统概率神经网络(Probabilistic Neural Net-work,PNN)模型和以算法识别率为优化目标的BA-PNN扰动判定模型,并结合数字孪生技术将BA-PNN模型融入孪生平台;最后通过仿真与结果分析,对比优化前模型效果及孪生平台特点,该模型识别效果较之前显著提高,证明了方法的有效性。

基于改进BA-PNN的智能变电站二次设备故障定位方法

针对概率神经网络(PNN)在二次设备故障定位中训练规模较大、容易受到平滑因子干扰的问题,提出了一种基于改进蝙蝠算法优化概率神经网络(BA-PNN)的智能变电站二次设备故障定位方法。首先,在PNN的求和层中采用拉普拉斯分布代替高斯分布,并用BA算法来获得最优平滑因子,进而提出一种改进蝙蝠算法优化概率神经网络方法;其次,基于智能变电站中二次设备的特征分析,选择故障特征量并对其映射,建立了基于BAPNN的智能变电站二次设备故障定位模型;最后,以某智能变电站故障定位为例,对BA-PNN神经网络进行样本训练,实现对故障元件的精确定位。仿真表明,该方法缩小了神经网络的训练规模,提升了神经网络的计算性能,增强了故障定位的准确性。

基于PNN神经网络的凿岩台车电液控制系统故障诊断研究

针对凿岩台车电液控制系统故障诊断效率低的问题,该文提出一种结合故障树分析法和概率神经网络(probabilistic neural network,PNN)的故障诊断方法。首先,基于电液控制系统的结构和工作原理构建其故障树模型;然后通过对故障树模型进行定性分析,确定其最小割集和典型故障种类,以选取的典型故障种类的关键参数构建故障征兆矩阵,通过PNN神经网络对该矩阵进行训练和计算,实现对系统典型故障状态的自动识别。实验结果表明,该文方法的平均诊断时间为1.2 s,平均诊断准确率为80%,能够快速准确地定位系统故障,可满足凿岩台车电液控制系统故障诊断的工程实际需求。

基于IWOA-PNN模型的生物组织变性识别方法

为了提高高强度聚焦超声(HIFU)治疗过程中生物组织变性识别率,提出了一种基于改进鲸鱼优化算法优化概率神经网络(IWOA-PNN)模型的生物组织变性识别方法。首先通过改进收敛因子和加入自适应权重因子提高WOA优化算法的寻优速度和精度,然后利用IWOA算法优化PNN的平滑因子,以提高变性识别精度,最后以超声回波信号多尺度熵为特征参数输入IWOA-PNN模型,得出生物组织变性识别率。实验结果表明,与普通PNN和WOA-PNN模型相比,基于IWOA-PNN模型的生物组织变性识别率更高,更能精确地识别HIFU治疗过程中生物组织是否变性,指导临床医生进行准确的HIFU疗效评价。

Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks

The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 min to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.

利用多层次网眼特征和VAE-PNN模型识别城市道路格网模式

作为道路网中普遍存在的显式模式之一,格网模式蕴含了丰富的城市空间格局信息,识别道路格网模式是实现自动化、智能化地图综合的关键前提。针对现有格网模式识别方法较少考虑多层次网眼特征,存在训练样本多样性不足等问题,本文提出一种基于多层次网眼特征和VAE-PNN模型的城市道路格网模式识别方法。首先,对原始路网数据进行化简;然后,设计了内部正交函数、格网形态描述和邻域相关关系的多层次网眼特征,进而利用变分自编码器(VAE)增强训练样本多样性;最后,借助概率神经网络(PNN)模型实现道路格网模式分类识别。试验结果表明,综合考虑多层次网眼特征能够准确识别不同类型、不同形态的道路格网模式,通过VAE样本增强有效提升分类模型性能和格网模式识别精度。

农网台区线路异常的自适应DBSCAN-PNN诊断方法

新型电力系统建设背景下,线路异常诊断对于实现农网台区的线损管理和线路健康状态评价意义更为显著。针对当前农网台区缺少数字化线路异常诊断手段的问题,提出了一种基于自适应DBSCAN-PNN的农网台区线路异常诊断方法。首先,获取异常用户的虚拟回路阻抗计算结果;其次,采用K近邻方法自适应选取DBSCAN参数,并结合各类阻抗异常形成的专家先验知识规律,构建典型农网台区线路异常样本数据集;进一步将样本数据集按照一定的比例拆分为训练集和测试集,送入PNN分类模型中进行训练与测试,输出典型异常分类结果。最后,以某地区4类典型异常案例为基础进行案例分析,结果证明该方法异常诊断准确率能够快速实现典型农网台区线路异常诊断的快速精准识别,辅助支撑农网台区线损的精益化运维管理。

基于IHHO-PNN的变压器复合故障诊断

为了提高变压器复合故障诊断精度,提出了一种基于改进哈里斯鹰(Improved Harris Hawk Optimization, IHHO)算法优化概率神经网络(Probabilistic Neural Network, PNN)的变压器复合故障诊断方法。采用Tent映射、非线性调整逃逸能量和小孔成像学习策略对哈里斯鹰优化(Harris Hawk Optimization, HHO)算法进行改进,以增强IHHO算法的优化性能,避免算法陷入局部最优。采用IHHO算法对PNN的平滑因子进行优化,建立了基于IHHO-PNN的变压器故障诊断模型。利用实际运行的变压器故障数据进行仿真分析。结果表明,所提出的IHHO-PNN模型在进行变压器故障诊断时出现错误诊断的次数更少,诊断精度更高,变压器故障诊断效果好于其他几种对比模型,验证了该变压器复合故障诊断方法的实用性和有效性。

基于IWOA-PNN模型的管道焊缝腐蚀剩余强度预测

针对管道焊缝腐蚀问题构建基于改进鲸鱼优化算法(Improved Whale Optimization Algorithm, IWOA)的概率神经网络(Probabilistic Neural Network, PNN)剩余强度预测模型。首先,通过种群初始化、非线性收敛因子和惯性权重因子提高鲸鱼优化算法的寻优速度和精度;然后,利用IWOA算法优化PNN的光滑因子,构建IWOA-PNN预测模型;最后,以水压爆破试验数据为基础,使用MATLAB软件进行仿真试验,并与另外2个模型进行对比分析。结果表明:IWOA-PNN模型的ERMS为0.633 1,EAR为2.19%,R^(2)为0.954 6,均优于PNN和鲸鱼优化算法(Whale Optimization Algorithm, WOA)-PNN模型;IWOA-PNN模型与传统模型相比误差更小,能够更为准确地预测焊缝腐蚀后剩余强度,为管道的维修和更换提供参考。

IPP-PNN模型在川藏铁路深埋长大隧道岩爆预测中的应用

为了准确预测在高地应力、高地温铁路隧道中的岩爆灾害,以川藏铁路前期拉林段的重要隧道节点工程为研究背景,系统、全面地总结应力水平、埋深、温度、围岩岩性及地质构造、岩体系统刚度等影响因素对川藏铁路深埋长大隧道岩爆的孕育作用,重点分析高地应力和高地温对岩爆发生的影响相关性。构建川藏铁路深埋长大隧道岩爆预测指标体系,测试并量化岩体岩爆的倾向性指标。由于各影响因素与岩爆的非线性关系,选用能充分提取数据信息、处理多因素复杂非线性问题的改进投影寻踪(Improved Projection Pursuit,IPP)评价模型对川藏铁路拉林段典型高地应力、高地温深埋长大隧道桑珠岭隧道在施工期发生的岩爆问题做初步评价,并引入密度函数估计和贝叶斯最小风险准则,将IPP模型和概率神经网络(Probabilistic Neural Networks,PNN)模型相结合,实现对岩爆等级的聚类划分。研究结果表明:根据岩爆等级预测结果可知IPP-PNN模型预测结果相比于传统PP-PNN模型和GSA-PP模型其准确度更高,在对桑珠岭隧道11~19号隧道路段的岩爆预测中,岩爆预测等级与实测等级相符合程度由66.67%和77.78%提高到100%。研究结果具有一定的应用价值和工程意义,为目前在建的川藏铁路类似隧道工程的岩爆预测提供参考。