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.

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.

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.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

Underwater Positioning Based on an Artificial Lateral Line and a Generalized Regression Neural Network

Taking advantage of the lateral line organ, fish can navigate, feed, and avoid predators and obstacles by sensing surrounding flow fields. The lateral line organ provides an important reference for the development of new underwater detection technology. Inspired by the lateral line organ, in this paper, for the sake of localizing the target dipole source in three-dimensional underwater space, an artificial lateral line consisting of nine underwater pressure sensors forming a cross-shaped sensor array is applied. Combined with the method of gener- alized regression neural network, which is suitable for solving nonlinear pattern recognition problems, a corresponding experimental platform has been built to sample data for training the neural network from a 12 cm by 12 cm by 24 cm cuboid space. The experimental results indicate that the cross-shaped artificial lateral line can localize the target dipole source two body-lengths away. The well- performing perceptual distance is below 13 cm away from the sensing array. Moreover, decreasing the data sampling interval and in- creasing the number of sensors utilized can help improve the positioning accuracy.

Short-term Load Forecasting of Regional Distribution Network Based on Generalized Regression Neural Network Optimized by Grey Wolf Optimization Algorithm

Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong randomness.This paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input features.After that the high-precision short-term丨oad forecasting based on GWO-GRNN model is realized.GRNN is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast convergence.Finally a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.

Short-term traffic forecasting based on principal component analysis and a generalized regression neural network for satellite networks

With the rapid growth of satellite traffic, the ability to forecast traffic loads becomes vital for improving data transmission efficiency and resource management in satellite networks. To precisely forecast the short-term traffic loads in satellite networks, a forecasting algorithm based on principal component analysis and a generalized regression neural network （PCA-GRNN） is proposed. The PCA-GRNN algorithm exploits the hidden regularity of satellite networks and fully considers both the temporal and spatial correlations of satellite traffic. Specifically, it selects optimal time series of spatio-temporally correlated historical traffic from satellites as forecasting inputs and applies principal component analysis to reduce the input dimensions while preserving the main features of the data. Then, a generalized regression neural network is utilized to perform the final short-term load forecasting based on the obtained principal components. The PCA-GRNN algorithm is evaluated based on real-world traffic traces, and the results show that the PCA-GRNN method achieves a higher forecasting accuracy, has a shorter training time and is more robust than other state-of-the-art algorithms, even for incomplete traffic datasets. Therefore, the PCA- GRNN algorithm can be regarded as a preferred solution for use in real-time traffic forecasting for realistic satellite networks.

QSAR study on estrogenic activity of structurally diverse compounds using generalized regression neural network

Computer-based quantitative structure-activity relationship (QSAR) model has been becoming a pow- erful tool in understanding the structural requirements for chemicals to bind the estrogen receptor (ER), designing drugs for human estrogen replacement therapy, and identifying potential estrogenic endo- crine disruptors. In this study, a simple yet powerful neural network technique, generalized regression neural network (GRNN) was used to develop a QSAR model based on 131 structurally diverse estro- gens (training set). Only nine descriptors calculated solely from the molecular structures of com- pounds selected by objective and subjective feature selections were used as inputs of the GRNN model. The predictive power of the built model was found to be comparable to that of the more traditional techniques but requiring significantly easy implementation and a shorter computation-time. The ob- tained result indicates that the proposed GRNN model is robust and satisfactory, and can provide a feasible and practical tool for the rapid screening of the estrogenic activity of organic compounds.

Term Structure of Interest Rates Based on Artificial Neural Network

In light of the nonlinear approaching capability of artificial neural networks （ ANN）, the term structure of interest rates is predicted using The generalized regression neural network （GRNN） and back propagation （BP） neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek＇s model and Cox-Ingersoll-Ross （CIR） model. The comparison reveals that neural network models outperform Vasicek＇s model and CIR model, which are more precise and closer to the real market situation.

基于可见光谱结合神经网络算法快速鉴别特级初榨橄榄油

随着中国经济的不断繁荣,人民对物质生活水平提出了更高的要求,预防疾病、改善身体功能的食品成为当前消费市场的“热点”。油脂能提供人体所必需的能量,食用油是人类获取油脂的主要途径之一,而高品质植物油含有对人体健康更有益的物质,例如单不饱和脂肪酸、多酚、角鲨烯、维生素E等营养物质。由于采用物理冷榨工艺,特级初榨橄榄油几乎保留了其橄榄果中所有的营养物质,油酸含量高达70%。因此,虽然作为一种“舶来品”,特级初榨橄榄油进入中国市场后一直是植物油市场中的“宠儿”,其价格也明显高于市场上的普通植物油。在利益的驱动下,特级初榨橄榄油的制假贩假现象屡禁不止,制假贩假的手段也不断更新迭代,从而造成国内橄榄油市场假冒伪劣产品屡禁不止,掺假的油品不仅会对消费者的生命财产造成伤害,而且也会影响合法经营者的生产和销售,扰乱销售市场,破坏市场秩序,影响民众对特级初榨橄榄油的认可度。为实现特级初榨橄榄油掺伪量的快速、准确、低成本地检测,提出一种基于广义回归神经网络结合紫外可见光谱实现植物油定性定量分析方法。广义回归神经网络在学习速度和非线性映射能力上表现出色,且扩散因子是其网络的唯一优化参数,不需要反向传播和反复迭代。与其他检测技术相比,紫外可见光谱技术在检测周期、稳定性、低维护成本等方面具有压倒性优势。通过两种方法的联用在植物油定性鉴别中实现了100%的判别,在特级初榨橄榄油掺伪定量检测中实现了判定系数R2优于0.98875,均方根误差RMSE优于0.03833的结果。研究结果表明,该模型在植物油种类鉴别及特级初榨橄榄油掺伪定量检测中表现出优秀的预测能力。

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。

一种基于智能算法的GNSS高程拟合方法

广义回归神经网络(GRNN)是一种新型的前馈神经网络模型,具有训练次数少、耗时短、非线性参数的预报能力较强等优点。但GRNN唯一的调节参数SPREAD不能自动获取限制其进一步的应用。针对该缺陷,本文采用果蝇优化算法(FOA)与GRNN相结合构建FOAGRNN模型对GRNN进行优化,自动获取调节参数的值。为了检验FOAGRNN模型的GNSS高程拟合精度,进行了实验分析。实验结果证明了FOAGRNN模型的GNSS高程拟合精度可达6mm。为进一步检验FOAGRNN模型的优越性,采用与平面拟合模型、二次曲面拟合模型进行对比。实验结果表示FOAGRNN模型的拟合精度要优于平面拟合模型和二次曲面拟合模型,证明了FOAGRNN模型在数据样本较少的情况下,其GNSS高程拟合精度仍然可以达到较高精度。

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。

基于混合集成学习模型的测井曲线生成方法

测井曲线在储集层评价和油气资源评估中具有十分重要的作用,但是实际应用中经常出现部分测井曲线缺失的情况,而重新测井的成本高昂且实现困难。为了在不增加经济成本的基础上补充缺失的测井曲线,提出了一种基于混合集成学习模型的测井曲线生成方法,以高效智能的方式补全缺失的测井曲线。混合集成学习模型结合了随机森林模型和极限梯度提升模型的结构优势,深度挖掘测井数据之间的非线性映射关系,实现了对测井曲线的精准生成。将混合集成学习模型应用于实际测井数据,并将其生成结果与全连接神经网络模型和多元线性回归模型的生成结果进行对比分析,实验结果表明混合集成学习模型合成的人工测井曲线精度更高,说明了混合集成学习模型适用于生成测井曲线,为人工测井曲线合成提供了一种新的思路。

全球气温–相对湿度–人口驱动型制冷度日数时空演变、影响因素及模拟

制冷度日数(Cooling degree days,CDDs)可指示空间制冷能耗与室外热环境,但在全球栅格尺度上同时考虑气温、相对湿度与人口的CDDs分析鲜见报道。据此,本文利用气象、人口、遥感等数据,曼−肯德尔法、相对重要性分析、机器学习等方法在全球0.25°栅格尺度上开展气温−相对湿度−人口驱动型CDDs时空变化、影响因素与模拟研究。结果表明,①全球基于湿球温度计算的CDDs(CDDs_(wb),CDDs based on wet bulb temperature)在30°N~30°S间除北非与西亚外的不少地区均高于567(℃·d),极高值[1469~2677(℃·d)]主要分布在亚马孙平原、东南亚中南半岛南侧及其以南地区。基于湿球温度与人口计算的CDDs(CDDs based on wet bulb temperature and population,CDDs_(wb_pop))大多低于17×10^(6)(℃·d·人),高值[277×10^(6)~2144×10^(6)(℃·d·人)]主要在恒河平原与印度南端、尼日利亚沿海、越南南北平原与爪哇岛。②1970—2018年CDDs_(wb)与2000—2018年CDDs_(wb_pop)在中高纬度呈现极高年际间变异,全球未来变化趋势多与过去保持强一致性。CDDs_(wb)显著增加(P<0.05)地区主要分布在北非与西亚、澳大利亚、里海东部、印尼西部的一些地区,显著降低区域主要分布在拉美、撒哈拉以南非洲、中国胡焕庸线以南及中南半岛的一些地区。CDDs_(wb_pop)在一些地区显著增加,速率基本小于8×10^(6)(℃·d·人)/a,集中发布在北非、西亚与里海东部的一些地区。③纬度与高程均分别与CDDs_(wb)及其变异系数呈现显著负向与正向偏相关关系(P<0.05);在不同大洲内,年降水量、夏季反照率、增强型植被指数与PM_(2.5)对CDDs_(wb)影响不同,夜间灯光影响不大。CDDs_(wb)实际值与模拟值间R2大多高于0.935,平均绝对误差百分比多小于6.77%,均方根误差在15.63~184.51(℃·d)。