Electricity price forecasting using generalized regression neural network based on principal components analysis

A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.

Risk based security assessment of power system using generalized regression neural network with feature extraction

A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.

Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model

The general regression neural network(GRNN) model was proposed to model and predict the length of day(LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum(AAM) function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.

Performance Prediction of Switched Reluctance Motor using Improved Generalized Regression Neural Networks for Design Optimization

Since practical mathematical model for the design optimization of switched reluctance motor(SRM)is difficult to derive because of the strong nonlinearity,precise prediction of electromagnetic characteristics is of great importance during the optimization procedure.In this paper,an improved generalized regression neural network(GRNN)optimized by fruit fly optimization algorithm(FOA)is proposed for the modeling of SRM that represent the relationship of torque ripple and efficiency with the optimization variables,stator pole arc,rotor pole arc and rotor yoke height.Finite element parametric analysis technology is used to obtain the sample data for GRNN training and verification.Comprehensive comparisons and analysis among back propagation neural network(BPNN),radial basis function neural network(RBFNN),extreme learning machine(ELM)and GRNN is made to test the effectiveness and superiority of FOA-GRNN.

An Approach to Carbon Emissions Prediction Using Generalized Regression Neural Network Improved by Genetic Algorithm

The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change policy.Through the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of GRNN.Genetic algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN.During the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into account.The target values were obtained after the calculation of the predicted results.Finally,compared with the results of GRNN,the improved method realized higher prediction accuracy.It thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.

Application of generalized regression neural network on fast 3D reconstruction

In robot-assisted surgery projects,researchers should be able to make fast 3D reconstruction. Usually 2D images acquired with common diagnostic equipments such as UT, CT and MRI are not enough and complete for an accurate 3D reconstruction. There are some interpolation methods for approximating non value voxels which consume large execution time. A novel algorithm is introduced based on generalized regression neural network (GRNN) which can interpolate unknown voxles fast and reliable. The GRNN interpolation is used to produce new 2D images between each two succeeding ultrasonic images. It is shown that the composition of GRNN with image distance transformation can produce higher quality 3D shapes. The results of this method are compared with other interpolation methods practically. It shows this method can decrease overall time consumption on online 3D reconstruction.

Parameters optimization for exponentially weighted moving average control chart using generalized regression neural network

As a useful alternative of Shewhart control chart, exponentially weighted moving average （EWMA） control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA （MEWMA） control charts, in which the optimal parameter pair （ λ, k） or （ λ, h ） was searched by using the generalized regression neural network （GRNN）. The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length （ARLo） and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported.

Modelling the impact of climate change on rangeland forage production using a generalized regression neural network:a case study in Isfahan Province,Central Iran

Monitoring of rangeland forage production at specified spatial and temporal scales is necessary for grazing management and also for implementation of rehabilitation projects in rangelands. This study focused on the capability of a generalized regression neural network（GRNN） model combined with GIS techniques to explore the impact of climate change on rangeland forage production. Specifically, a dataset of 115 monitored records of forage production were collected from 16 rangeland sites during the period 1998–2007 in Isfahan Province, Central Iran. Neural network models were designed using the monitored forage production values and available environmental data（including climate and topography data）, and the performance of each network model was assessed using the mean estimation error（MEE）, model efficiency factor（MEF）, and correlation coefficient（r）. The best neural network model was then selected and further applied to predict the forage production of rangelands in the future（in 2030 and 2080） under A1 B climate change scenario using Hadley Centre coupled model. The present and future forage production maps were also produced. Rangeland forage production exhibited strong correlations with environmental factors, such as slope, elevation, aspect and annual temperature. The present forage production in the study area varied from 25.6 to 574.1 kg/hm^2. Under climate change scenario, the annual temperature was predicted to increase and the annual precipitation was predicted to decrease. The prediction maps of forage production in the future indicated that the area with low level of forage production（0–100 kg/hm^2） will increase while the areas with moderate, moderately high and high levels of forage production（≥100 kg/hm^2） will decrease both in 2030 and in 2080, which may be attributable to the increasing annual temperature and decreasing annual precipitation. It was predicted that forage production of rangelands will decrease in the next couple of decades, especially in the western and southern parts of Isfahan Province. These changes are more pronounced in elevations between 2200 and 2900 m. Therefore, rangeland managers have to cope with these changes by holistic management approaches through mitigation and human adaptations.

Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine

Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.

Prediction of Water Table Based on General Regression Neural Network

Traditional methods for water table prediction have such defects as extensive calculation and reliance on the presupposition of a homogeneous and regular aquifer.Based on the fundamentals of the general regression neural network(GRNN),this article sets up a GRNN model for water level prediction.Case study indicates that this model,even with limited information,has satisfactory prediction accuracy,which,coupled with a simple model structure and relatively high calculation efficiency,mean a vast application prospect for the model.

改进的MVO-GRNN神经网络岩爆预测模型研究

准确预测岩爆烈度等级能有效指导岩爆灾害的防控。根据影响岩爆发生及烈度等级的3个因素构建岩爆评价指标体系,提出一种基于改进多元宇宙算法(Improved Multi-Verse Optimizer,IMVO)优化广义回归神经网络(General Regression Neural Network,GRNN)的岩爆预测模型。在普通多元宇宙算法(MVO)的基础上,运用自适应平衡机制调节MVO算法中的虫洞存在概率(V_(WEP))和旅行距离率(V_(TDR))两个重要参数来改进该算法;再运用改进的多元宇宙算法优化广义回归神经网络的光滑度,通过训练数据优选出最佳光滑因子σ,得到IMVO-GRNN神经网络岩爆烈度预测模型;最后结合工程实例验证模型的性能。研究表明,该模型相比传统模型寻优能力更强,精度更高,为岩爆预测提供了一种新的思路。

基于GRNN-MC的变压器振动信号预测

变压器振动信号是评估其工作状态的重要参数之一,与绕组松动或变形等隐患密切相关,为揭示变压器振动信号的变化趋势,本文提出了一种基于广义回归神经网络和马尔科夫链修正的变压器振动信号预测方法。即分别以变压器运行电压、负载电流和振动信号归一化特征频率为输入和输出建立变压器振动信号广义回归神经网络预测模型,然后引入马尔科夫链并结合负载电流的变化对振动信号计算结果进行修正以获得最终的预测结果。对某500 kV变压器振动在线监测信号的分析结果表明:经马尔科夫链修正后的变压器广义回归神经网络振动信号预测模型预测精度高,可为变压器绕组运行状态的振动监测技术提供重要参考。

基于改进GWO-GRNN的管道焊缝三维重构测量

为提高双目相机不同位姿下焊缝的三维重构测量精度,提出一种基于立体视觉图像误差补偿的管道焊缝三维重构测量方法。采用改进灰狼算法(IGWO)优化广义回归神经网络(GRNN)补偿焊缝三维重构图像点的坐标误差。采用混沌映射、非线性收敛因子和最优记忆保存思想对GWO算法进行改进,通过8个标准测试函数进行仿真验证;利用优化后的GRNN模型对图像点坐标误差进行预测和补偿,计算三维坐标重构出焊缝点云,三维测量焊缝的焊宽、余高和长度。试验结果表明:该模型在双目相机不同的位姿状态下都能较准确地实现焊缝的三维重构,焊缝的三维测量相对误差在0.9%以内。

基于改进GRNN的光纤光栅解调算法研究

木材干燥室的干燥温度监测是木材加工厂的重要防火措施之一,FBG传感器的材料和传感特性使其可以监测干燥室内部温度而不会成为新的火灾诱因。针对FBG传感器波长解调过程中直接解调精度低的缺点,研究了一种基于柯西分布曲线拟合和SENet改进GRNN在光纤光栅传感器的解调算法。利用柯西曲线拟合和希尔伯特变换分割FBG反射曲线峰值区域,将峰值区域数据组成改进GRNN模型的训练样本和测试样本,将SENet结构嵌入GRNN模型提升GRNN模型的性能,通过PSO算法获取改进GRNN的最优光滑因子。实验结果表明,解调温度误差约为0.15℃,波长误差为1.8 pm,与直接解调法和未改进的GRNN模型相比,嵌入SENET结构的改进GRNN模型解调对比PSO-GRNN法和SE-GRNN法,性能分别提升约76%和约84%,有效提升了光纤光栅传感器的解调精度。

利用地震震级样本数据验证PCA-GSM-GRNN模型的优越性

针对地震震级与其影响指标之间的非线性问题,提出了基于网格搜索法(GSM)和主成分分析法(PCA)优化广义回归神经网络(GRNN)的地震预测模型。采用PCA对震级影响指标进行维度约简,将降维后的主成分作为模型输入向量,地震震级作为模型输出向量,同时选用GSM寻优GRNN最佳参数,利用学习样本对新模型进行训练,最终构建基于PCA-GSM-GRNN的地震震级预测模型。将PCA-GSM-GRNN模型应用于测试样本,结果显示:PCA-GSM-GRNN模型预测结果准确率相较于GRNN-GSM和GRNN模型分别提高5.03%和5.66%,具有良好的预测效果。

基于SSA-GRNN的铜CMP抛光液抛光速率预测

铜化学机械抛光(CMP)是集成电路制造的关键步骤之一,其中铜抛光速率是衡量抛光液性能的关键指标。在CMP过程中,由于铜抛光液中各组分与铜之间的化学反应复杂,需要大量的数据实验来实现可调的抛光速率。为提高铜CMP抛光速率预测的准确性,利用麻雀搜索算法对广义回归神经网络的平滑因子进行优化,提出了一种基于麻雀搜索算法的广义回归神经网络(SSA-GRNN)铜CMP抛光液抛光速率预测模型。首先,在MATLAB中建立SSA-GRNN网络模型,然后输入抛光液各组分数据,预测在不同组分下抛光液的抛光速率,最后将SSA-GRNN模型的预测结果与BP神经网络模型(BP-NCABC)的预测结果对比。结果表明,SSA-GRNN模型在训练集上的平均绝对百分比误差(MAPE)比BP-NCABC模型降低4.82百分点,在测试集上的MAPE比BP-NCABC模型降低1.78百分点;SSA-GRNN模型在训练集上的最优预测精度比BP-NCABC模型提高0.09百分点,在测试集上的最优预测精度比BP-NCABC模型提高0.32百分点。上述研究结果表明,在CMP抛光速率的预测上SSA-GRNN模型比BP-NCABC模型的准确性更高,这为指导CMP实验、提升实验效率、降低研发成本和优化抛光液组分提供了一种可选的模型。

基于PSO-GRNN的含钛高炉渣活化焙烧浸出成分预测模型

活化焙烧是一种回收利用含钛高炉渣中钛资源的新方法。为通过反应条件快速获得回收渣中成分含量,建立了基于粒子群优化的广义回归神经网络(PSO-GRNN)预测模型。借助斯皮尔曼(Spearman)相关性分析筛选特征变量作为模型输入,利用PSO优化GRNN神经网络的权重与节点阈值,通过与偏最小二乘回归(PLS)、随机森林(RF)以及支持向量回归(SVR)算法的对比,确定了提出模型的优势。研究结果表明,PSO-GRNN具有最小的RMSE和最大的R2,表明在该数据集上所设计的PSO-GRNN有最佳的模型性能,可以为后续实验或工业应用提供一定的指导。

面向磨损检测的切削表面粗糙度评估及AGRNN预测

当前切削表面粗糙度大多需要结合人工经验以及多次测试方法,加工质量难以得到保障。在充分发挥加工阶段历史参数作用的基础上,构建了磨损监测模型。同时为了满足算法精度以及响应速率的要求,引入了快速响应和逼近的自适应广义回归神经网络(AGRNN)进行粗糙度预测。研究结果表明:计算得到粗糙度预测数据和实际值相关系数达到R^(2)=0.988,预测模型达到了理想的控制状态,预测精度满足调控标准,经过设备调节后可以继续缩短响应时间。在主轴转速1000~2000 r/min、进给量0.2~0.3 mm/r、轴向切深0.2~0.4 mm、径向切深1~5 mm范围内,AGRNN对应的磨损与粗糙度MAPE依次为3.685和2.236,低于卷积神经网络(CNN)、高斯过程回归(GPR)、支持向量机(SVM)和多元线性回归(MLR)4种算法,达到了理想预测效果,控制决策时间也明显缩短。

基于LPSO-GRNN模型的螺栓松紧状态预测研究

在轴重式动态汽车衡的服役状态下,由于受到重型货车频繁的加卸载循环冲击,会导致其内部螺栓发生松弛脱落,针对这一问题,提出了一种基于莱维飞行改进粒子群算法优化的广义回归神经网络(LPSO-GRNN)的轴重式动态汽车衡螺栓松紧状态预测模型,并结合振动信号特征提取,将该模型应用于汽车衡螺栓松紧状态的预测。首先,研究并提取了螺栓不同松紧状态下输出振动信号的波形指标、峰值指标、脉冲指标、峭度指标等信号特征,并将其作为模型的共同输入特征向量;然后,采用莱维飞行提高了粒子群优化算法的寻优能力,通过产生随机步长,提高了算法的全局寻优能力,避免算法陷入局部最优值;利用改进的算法对广义回归神经网络(GRNN)的光滑因子进行了优化,得到了全局最优的光滑因子;最后,通过设计实验,分别使用广义回归神经网络(GRNN)、粒子群算法优化广义回归神经网络(PSO-GRNN)和LPSO-GRNN,以此来对螺栓松紧状态进行了预测,并将预测结果与实际情况进行了对比分析。实验结果表明:基于LPSO-GRNN建立的螺栓松紧状态预测模型,其预测准确率可达到95%。研究结果表明:该螺栓松紧状态预测模型可以有效提高汽车衡螺栓松紧预测的准确率,同时有效解决粒子群算法容易陷入局部最优收敛的问题。

GRNN神经网络在汽车发动机性能预测中的应用

建立多输入参数条件下发动机动力性能及燃油经济性能预测模型,研究平滑因子、输入参数对预测精度的影响;建立预测模型,研究发动机运转参数对动力性能与燃油消耗率的影响规律。研究结果表明:采用广义回归神经网络(GRNN)能构建准确性较高的发动机动力性能与燃油经济性能预测模型;选择合适的平滑因子可使GRNN算法获得的预测值避免出现较大波动,同时兼顾较高预测精度;保持合适的油门开度能使发动机输出较高的功率和转矩;低功率或低油门开度使发动机燃油消耗率较高。