基于径向基函数神经网络的涡流传感器非线性补偿方法研究

为了解决数字式涡流传感器的非线性问题,提出利用径向基函数神经网络进行非线性补偿的方法。介绍非线性补偿原理以及算法,并将其与BP神经网络法进行比较。从实测数据出发,建立了涡流传感器的非线性补偿模型。结果表明,这种非线性补偿模型误差小、有良好的鲁棒性、能实现在线软补偿,比用BP神经网络有更快的训练速度。     

基于径向基函数神经网络的配电网参数估计

配电网的线路参数会受温度、周围环境、集肤效应等因素的影响而发生变化,导致在进行配电网常规分析应用时降低计算结果的精度。本文提出一种基于径向基函数神经网络的三相不平衡配电网线路参数估计方法,通过对配电网三相不平衡线路等效模型的数学推导建立参数估计模型,利用径向基函数神经网络去拟合线路两端支路功率、节点电压和线路参数之间的非线性关系。对于训练完成的径向基函数神经网络,只需要知道线路两端测量值便可以获得准确的线路参数,可以有效地解决线路参数,估计数学模型中的病态矩阵问题。     

基于径向基函数神经网络的电容传感器非线性辨识

分析差动变间隙式电容传感器的非线性因素,提出基于径向基函数神经网络的传感器非线性辨识的算法、方案与实现技术.对电容传感器进行实验,通过计算机仿真与应用,实现了实验过程和实验数据处理的智能化和简单化,能有效地辨识传感器的非线性,从而提高了测量的精度和速度.     

水轮机轴承非线性油膜力的径向基神经网络算法

通过对基于免疫原理的RBF神经网络在线学习算法的改进,利用网络的非线性逼近能力,对水轮机组轴系中非线性轴承油膜力进行计算,建立了从轴承状态参数到油膜力的非线性映射关系。并通过实例验算证明,可弥补原算法中较难选择初始参数的缺陷,加上对个别计算法则作的修改,使其更符合RBF神经网络工作的物理特性,从而拓展了机组轴承油膜力的计算方法。     

基于径向基函数神经网络的中速磨煤机故障诊断

为了能够对磨煤机早期故障做出预测并有效判别故障类型,提出了基于径向基函数神经网络的磨煤机故障诊断方法。介绍了该方法可以有效地处理故障征兆与故障类型之间的不确定性,具有很好的分辨力。应用该方法对某电厂HP碗式中速磨煤机的故障特征数据集进行了仿真实验,表明该方法故障诊断正确率高,诊断结果是有效的。     

基于径向基函数神经网络的在线分布式故障诊断系统

作者建议使用分布式智能系统解决大规模电力网络的实时故障诊断问题,并为此提出了一种新的基于最小度排序的图形分割方法,它能够将大规模电力网络有效地分割为给定数目的连通子网络,并且各子网络的故障诊断负担近似相等,同时每个网络边界元件的数目最小。然后用径向基函数神经网络完成各子网络的故障诊断。所提出的分布式智能故障诊断系统已使用稀疏存储技术编程实现,并在IEEE14母线、30母线和118母线系统中进行了仿真研究。计算机仿真结果表明该故障诊断系统能有效地解决大规模电力网络的故障诊断问题。     

基于径向基函数神经网络的开关磁阻电机建模

基于径向基函数神经网络的局部逼近理论 ,利用高斯基函数 ,在分析测量数据和开关磁阻电机非线性磁特性的基础上 ,建立了开关磁阻电机的模型。通过与样机实测数据比较 ,验证了模型的有效性。与传统的局部线性化方法及BP神经网络比较 ,本文所建模型有更好的泛化能力和更快的速度 ,比较准确地反映了开关磁阻电机的磁特性 ,这对于开关磁阻电机的实时在线控制具有重要意义     

电力负荷的径向基函数神经网络模型预测

由于基于反向传播(back propagation,BP)的神经网络模型自身固有的缺点,其电力负荷预测结果不理想,而径向基函数(radial basis function,RBF)神经网络模型具有全局逼近的性质,不存在局部最小问题,为此,针对中长期电力负荷预测,给出了RBF的预测原理,推导权值的更新方式,并和BP方法结果进行对比分析,结果证明基于RBF神经网络模型的方法收敛速度快、预报精度高、误差小。     

基于径向基函数神经网络的电能质量综合评价

根据电能质量国家标准,对电能质量的各单项指标进行分级,并利用随机分布的原理随机生成了大量的样本用于训练神经网络,采用了非线性逼近能力很强的径向基函数(Radial Basis Function,RBF)神经网络建立了电能质量综合评价的模型,克服了模糊数学、概率论法以及层次分析法中的主观因素影响,提高了综合评价的客观性和合理性.通过对某地区变电站的电能质量指标测试结果进行评价,证明这种方法是合理可行的.     

基于径向基函数神经网络的SR电动机定子径向力建模

探讨了采用径向基神经网络对开关磁阻电动机定子径向力进行建模的方法。考虑到定子径向力模型中的两个输入量,即绕组电流和转子位置,取值范围较大,本文提出了先对输入量进行归一化处理,使得基函数的中心映射在[0,1]的闭区间内,再使用最近邻聚类和最速梯度下降法对网络进行训练的方法。文中给出了径向基神经网络和误差反传神经网络在建模精度和收敛速度上的比较,结果证实径向基函数神经网络除了具有很强的非线性逼近精度和泛化能力外,在给定同样的隐层神经元结构、网络学习率和目标误差,径向基神经网络在定子径向力非线性模型的训练过程中收敛速度更快,网络学习效率更高。     

Application of minimal radial basis function neural network todistance protection

The paper presents a new approach for the protection of power transmission lines using a minimal radial basis function neural network (MRBFNN). This type of RBF neural network uses a sequential learning procedure to determine the optimum number of neurons in the hidden layer without resorting to trial and error. The input data to this network comprises fundamental peak values of relaying point voltage and current signals, the zero-sequence component of current and system operating frequency. These input variables are obtained by a Kalman filtering approach. Further, the parameters of the network are adjusted using a variant of extended Kalman filter known as locally iterated Kalman filter to produce better accuracy in the output for harmonics, DC offset and noise in the input data. The number of training patterns and the training time are drastically reduced and significant accuracy is achieved in different types of fault classification and location in transmission lines using computer simulated tests     

Accurate fault locator for EHV transmission lines based on radial basis function neural networks

This paper describes the design and implementation of an artificial neural networks-based fault locator for extra high voltage (EHV) transmission lines. This locator utilizes faulted voltage and current waveforms at one end of the line only. The radial basis function (RBF) networks are trained with data under a variety of fault conditions and used for fault type classification and fault location on the transmission line. The results obtained from testing of RBF networks with simulated fault data and recorded data from a 400 kV system clearly show that this technique is highly robust and very accurate. The technique takes into account all the practical limitations associated with a real system. Thereby making it possible to effectively implement an artificial intelligence (AI) based fault locator on a real system.     

Structure selective updating for nonlinear models and radial basis function neural networks

Selective model structure and parameter updating algorithms are introduced for both the online estimation of NARMAX models and training of radial basis function neural networks. Techniques for on-line model modification, which depend on the vector-shift properties of regression variables in linear models, cannot be applied when the model is non-linear. In the present paper new methods for on-line model modification are developed. These methods are based on selectively updating the non-linear model structure and therefore lead to a reduction in computational cost. A real data set is used to demonstrate the performance of the new algorithms. © 1998 John Wiley & Sons, Ltd.     

基于径向基神经网络的可用输电能力概率分析

可用输电能力(ATC)是电力系统经济安全运行的一项重要指标.由于大量风电并网和用户用电行为的多样化,ATC的计算必须要考虑其带来的不确定源.而在对不确定源相关性的处理时,Nataf变换中标准正态分布域相关系数的求解尤为复杂,传统的基于辛普森数值积分和二分法的相关系数转换法耗时极其严重.在概率计算中,采用蒙特卡洛法基于最...     

Control of robots using radial basis function neural networks with dead‐zone

In this paper, we examine the control of robot manipulators utilizing a Radial Basis Function (RBF) neural network. We are able to remove the typical requirement of Persistence of Excitation (PE) for the desired trajectory by introducing an error minimizing dead‐zone in the learning dynamics of the neural network. The dead‐zone freezes the evolution of the RBF weights when the performance error is within a bounded region about the origin. This guarantees that the weights do not go unbounded even if the PE condition is not imposed. Utilizing protection ellipsoids we derive conditions on the feedback gain matrices that guarantee that the origin of the closed loop system is semi‐globally uniformly bounded. Simulations are provided illustrating the techniques. Copyright © 2011 John Wiley & Sons, Ltd.     

基于多输出衰减径向基函数神经网络的电网故障诊断

为提高电网故障诊断神经网络模型的构建速度,提出了一种基于多输出衰减径向基函数（Multi-output Decay Radial Basis Function, MDRBF）神经网络的故障诊断方法。DRBF神经网络不需训练即能以任意精度一致逼近任意连续多变量函数。介绍了单输出DRBF（Single-output DRBF, SDRBF）神经网络,分析了其存在的不足,即只能处理单输出变量问题,不能直接应用于电网故障诊断。在此基础上,根据电网元件的故障特点,提出了将SDRBF神经网络演变为多输出DRBF（Mu     

A radial basis function neural network controller for UPFC

This paper presents the design of radial basis function neural network controllers (RBFNN) for UPFC to improve the transient stability performance of a power system. The RBFNN uses either a single neuron or multi-neuron architecture and the parameters are dynamically adjusted using an error surface derived from active or reactive power/voltage deviations at the UPFC injection bus. The performance of the new single neuron controller is evaluated using both single-machine infinite-bus and three-machine power systems subjected to various transient disturbances. In the case of three-machine 8-bus power system, the performance of the single neuron RBF controller is compared with a BP (backpropagation) algorithm based multi-layered ANN controller. Further it is seen that by using a multi-input multi-neuron RBF controller, instead of a single neuron one, the critical clearing time and damping performance are improved. The new RBFNN controller for UPFC exhibits a superior damping performance in comparison to the existing PI controllers. Its simple architecture reduces the computational burden thereby making it attractive for real-time implementation     

Automatic determination of synergies by radial basis function artificial neural networks for the control of a neural prosthesis.

This paper describes an automatic method for synthesizing the control for a neural prosthesis (NP) that could augment elbow flexion/extension and forearm pronation/supination in persons with hemiplegia. The basis for the control was a synergistic model of reaching and grasping that uses temporal and spatial synergies between the arm and body segments. The synergies were determined from the movement data measured in nondisabled persons during the performance of functional tasks. The work space was divided into six zones: distance (two attributes) and laterality (three attributes). Radial basis function artificial neural networks (RBF ANN) were used to determine synergies. Sets of RBF ANN characterized with good generalization were selected as control laws for elbow flexion/extension and forearm pronation/supination. The validation was performed for three categories: inter-subject, distance, and laterality generalization. For all of the defined spatial synergies, the correlation was high for inter-subject and distance, yet low for the laterality scenario. This suggests the necessity for implementing different maps for different directions, but the same maps for different distances. The natural movements of the upper arm then drive the lower arm (elbow flexion/extension and forearm pronation/supination) in a way that is very well suited for the administration of functional electrical therapy (FET) in persons with hemiplegia soon after the onset of impairment.     

Fast voltage contingency screening using radial basis function neural network

Power system security is one of the vital concerns in competitive electricity markets due to the delineation of the system controller and the generation owner. This paper presents an approach based on radial basis function neural network (RBFN) to rank the contingencies expected to cause steady state bus voltage violations. Euclidean distance-based clustering technique has been employed to select the number of hidden (RBF) units and unit centers for the RBF neural network. A feature selection technique based on the class separability index and correlation coefficient has been employed to identify the inputs for the RBF network. The effectiveness of the proposed approach has been demonstrated on IEEE 30-bus system and a practical 75-bus Indian system for voltage contingency screening/ranking at different loading conditions.     

基于混合粒子群算法和NRBF神经网络的短期电价预测

提出一种混合粒子群算法,在局部邻近区域的粒子群算法中引入收缩因子和被动聚集,将最邻近聚类用于NRBF 神经网络的参数确定中,采用混合粒子群算法优化最近邻聚类的聚类半径,从而确定NRBF 神经网络的参数,提高了NRBF 神经网络的泛化能力。以美国PJM电力市场公布的2006年负荷与电价数据进行预测验证,证明了此方法所建立的模型的合理性和有效性。