Improved RBF network application in analog circuit fault isolation

One kind of steepest descent incremental projection learning algorithm for improving the training of radial basis function(RBF)neural network is proposed,which is applied to analog circuit fault isolation.This algorithm simplified the structure of network through optimum output layer coefficient with incremental projection learning(IPL)algorithm,and adjusted the parameters of the neural activation function to control the network scale and improve the network approximation ability.Compared to the traditional algorithm,the improved algorithm has quicker convergence rate and higher isolation precision.Simulation results show that this improved RBF network has much better performance,which can be used in analog circuit fault isolation field.

Prediction of coal ash fusion temperature using constructive-pruning hybrid method for RBF networks

A constructive-pruning hybrid method （CPHM） for radial basis function （RBF） networks is proposed to improve the prediction accuracy of ash fusion temperatures （AFT）. The CPHM incorporates the advantages of the construction algorithm and the pruning algorithm of neural networks, and the training process of the CPHM is divided into two stages： rough tuning and fine tuning. In rough tuning, new hidden units are added to the current network until some performance index is satisfied. In fine tuning, the network structure and the model parameters are further adjusted. And, based on components of coal ash, a model using the CPHM is established to predict the AFT. The results show that the CPHM prediction model is characterized by its high precision, compact network structure, as well as strong generalization ability and robustness.

CLASSIFICATIONS OF EEG SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK

Objective This paper presents classifications of m ental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) n etwork with optimal centers and widths for the Brain-Computer Interface (BCI) s chemes. Methods Initial centers and widths of the network are s elected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during train ing phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three t ask pairs over four subjects achieves 87.0%. Moreover, this network runs fast du e to the fewer hidden layer neurons. Conclusion The adaptive RB F network with optimal centers and widths has high recognition rate and runs fas t. It may be a promising classifier for on-line BCI scheme.

ERBF network with immune clustering

Based on immune clustering and evolutionary programming(EP), a hybrid algorithm to train the RBF network is proposed. An immune fuzzy C-means clustering algorithm (IFCM) is used to adaptively specify the amount and initial positions of the RBF centers according to input data set; then the RBF network is trained with EP that tends to global optima. The application of the hybrid algorithm in multiuser detection problem demonstrates that the RBF network trained with the algorithm has simple network structure with good generalization ability.

Performance prediction for Grid workflow activities based on features-ranked RBF network

Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural networkto predict the performance of Grid workflow activities.Experimental results for two kinds of real worldGrid workflow activities are presented to show effectiveness of our approach.

Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant

This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.

基于RBF网络的新疆特重雪灾区最大积雪深度预测研究

基于建立的雪灾灾损指数,确定新疆特重雪灾区域;进一步聚焦特重雪灾区的8个县(市),包括阿勒泰市、福海县、青河县、塔城市、托里县、沙湾市、尼勒克县和伊宁县,分别建立县域RBF网络模型,预测2021—2050年年最大积雪深度。结果表明:该模型可用于新疆特重雪灾区最大积雪深度预测,但预测精度仍有待提升;塔城市、尼勒克县将于2025—2029年连续出现最大积雪深度偏高事件,2039年青河县将出现最大积雪深度的极大值,因此应关注可能发生雪灾的年份与县(市),积极做好雪灾的防御工作。

Generating high-resolution climate maps from sparse and irregular observations using a novel hybrid RBF network

Sparse and irregular climate observations in many developing countries are not enough to satisfy the need of assessing climate change risks and planning suitable mitigation strategies.The wideused statistical downscaling model(SDSM)software tools use multi-linear regression to extract linear relations between largescale and local climate variables and then produce high-resolution climate maps from sparse climate observations.The latest machine learning techniques(e.g.SRCNN,SRGAN)can extract nonlinear links,but they are only suitable for downscaling low-resolution grid data and cannot utilize the link to other climate variables to improve the downscaling performance.In this study,we proposed a novel hybrid RBF(Radial Basis Function)network by embedding several RBF networks into new RBF networks.Our model can well incorporate climate and topographical variables with different resolutions and extract their nonlinear relations for spatial downscaling.To test the performance of our model,we generated high-resolution precipitation,air temperature and humidity maps from 34 meteorological stations in Bangladesh.In terms of three statistical indicators,the accuracy of high-resolution climate maps generated by our hybrid RBF network clearly outperformed those using a multi-linear regression(MLR),Kriging interpolation or a pure RBF network.

基于自适应RBF神经网络具有模型不确定性的四旋翼无人机指定时间预设性能控制方法

四旋翼无人机具有强耦合和欠驱动的特点,在飞行过程中很容易受到外界干扰,进而影响整个无人机系统的稳定性和精度。为此,提出了一种基于RBF神经网络的指定时间预设性能约束控制策略。首先,针对四旋翼无人机的不确定数学模型难以精确建立,并且在执行任务过程中存在外部未知扰动问题,提出了一种基于指定时间预设性能控制方法,将四旋翼无人机的轨迹跟踪问题转换为对位置子系统和姿态子系统的期望指令跟踪问题;其次,在设计控制器过程中,为了解决“微分爆炸”问题产生的滤波器误差,引入一种新型滤波误差补偿方法,通过RBF神经网络逼近外部未知扰动,并将预测结果补偿给控制器以提高轨迹跟踪的鲁棒性。最后,应用仿真模拟方法验证无人机控制系统稳定性和性能优势,通过飞行试验验证,微风聚拢环境下实际飞行轨迹与仿真模拟结果趋于一致,自主轨迹跟踪起降位置偏差小于1 cm,证明了所提出算法的有效性。

基于RBF神经网络的光伏并网系统自适应等效建模方法

针对广义负荷建模中的光伏并网系统模型难以适应不同逆变器控制和频率扰动的动态响应问题,提出了一种基于径向基函数(radialbasisfunction,RBF)神经网络的光伏并网系统自适应等效建模方法。首先,建立了光伏并网逆变器不同控制策略响应波形的检测判据。然后,构建了以电压-频率扰动为输入,有功功率和无功功率为输出的光伏并网系统RBF神经网络模型。最后,在Matlab/Simulink中搭建了光伏并网系统模型,并将其接入IEEE14节点配电网进行仿真验证。结果表明,构建的光伏并网自适应等效模型能够有效辨识电压频率给定控制、有功无功给定控制、下垂控制策略类型,能够准确反映光伏并网系统在不同电压、频率扰动下的有功功率、无功功率的动态响应特性。

基于IPSO-RBF神经网络的西北内陆河流域突发水污染风险评估

突发水污染事故会破坏环境、危害健康,开展西北内陆河流域突发水污染风险评估对于维护西部脆弱生态安全尤为重要。该文针对西北内陆河流域突发水污染问题,利用PSR模型遴选18个因素建立突发水污染风险评价指标体系,基于径向基神经网络模型(RBF)构建突发水污染风险评价模型。为进一步保证模型精度,采用改进惯性权重因子和学习因子的粒子群算法(IPSO)对神经网络模型参数进行优化,建立IPSO-RBF神经网络西北内陆河突发水污染风险评价模型,并运用该模型对石羊河流域武威段2017-2022年突发水污染进行风险等级评价。结果显示,石羊河流域武威段突发水污染2017-2019年风险等级为Ⅱ级,2020-2022年风险等级为Ⅲ级,结果与熵权-TOPSIS法一致,与流域治理情况相符。该研究成果有利于提升石羊河流域突发水污染的防控水平与应急处置能力,对于西北内陆河流域水资源管理以及祁连山生态保护具有重要意义。

基于RBF网络的足球点球轨迹预测

基于计算机视觉线性化轨迹预测模型在预测足球轨迹时,只能保证局部稳定性,存在轨迹跟踪局部稳定性问题和parking问题,提出了基于RBF网络的足球点球轨迹预测方法。建立足球运动状态传感信号解析模型,计算足球飞行地心重力、空气阻力、空气浮力、自身旋转时产生的马格努斯力的参数。建立足球飞行过程中的飞行受力解析模型,鉴于多参数模型复杂度过高,产生parking问题。利用RBF网络模型简化能力,建立飞行轨迹预测模型,结合并行滤波控制器,融合以上所有信息,在已知视觉概率计算的基础上,完成足球飞行轨迹的状态估计,并将其误差的协方差计算作为滤波控制的输入值,从而得到所有方差、均值的数据。最后获得足球在任意时刻的运动状态函数,完成预测。实验结果显示,该方法的足球运行轨迹吻合度为12 mm,且落点距离标准差最大仅为0.0412 m,因此,该预测方法能够得到精度更高的预测数据。

基于GA的RBF神经网络气液两相流持液率预测模型优化

为了提高气液两相流持液率预测精度,针对传统径向基函数(RBF)神经网络预测气液两相流持液率网络拓扑结构困难和收敛速度慢等问题,提出一种基于遗传算法(GA)优化径向基函数神经网络的气液两相流持液率预测模型。通过系统聚类算法和灰色关联度分析(GRA)对收集的实验数据进行处理,优选出最优模型特征,同时结合遗传算法确定了RBF神经网络结构参数。基于室内实验数据进行训练,并与常用于持液率预测的反向传播(BP)神经网络、GA-BP神经网络及RBF神经网络进行对比,评估了模型的准确性及可行性。结果表明:GA-RBF神经网络模型均方误差为0.0017,均方根误差为0.0416,平均绝对误差为0.0281,拟合度为0.9483。相较于其他神经网络模型,该预测模型表现出更高的计算精度和更强的泛化能力。

基于RBF神经网络滑模控制的卷纸纠偏系统

设计了采用RBF神经网络控制的伺服纠偏控制系统,通过建立其动力学模型,运用MATLAB/Simulink仿真软件仿真,并进行实验验证,分析系统动态性能,得到响应曲线。结果表明,在拉纸速度65 mm/s下,跑偏量从1.5 mm降低到0.55 mm,该伺服系统位移和速度跟踪误差均较小。

智能汽车轨迹跟踪MPC-RBF-SMC协同控制策略研究

针对自动驾驶车辆行驶过程中模型失配以及外部环境干扰导致车辆轨迹跟踪环节精确性不高的问题,提出了一种结合车辆运动学模型预测控制(MPC)、径向基(RBF)神经网络和滑模控制(SMC)的轨迹跟踪控制策略。通过建立车辆运动学MPC模型计算当前状态车辆期望横摆角速度,并将其与实际横摆角速度的偏差输入RBF-SMC控制器,利用RBF快速逼近非线性模型的特点,结合滑模控制输出前轮转角,实现车辆的横向轨迹跟踪控制。仿真结果表明,与传统的控制器相比,该方法轨迹跟踪精度显著提高,并在不同行驶工况下表现出较好的鲁棒性。

基于MI-PSO-RBF神经网络的铁路客货运量预测研究

准确地预测铁路客货运量对合理配置运输资源、提高铁路客货运组织工作效率有重要作用。为提高铁路客货运量的预测精度,提出一种基于MI-PSO-RBF神经网络的客货运量组合预测模型。本研究对铁路客货运量的影响因素及其内在关联进行分析,选取相关指标,利用互信息素法对指标进行筛选,构建影响因素指标体系。基于该指标体系,运用粒子群算法优化的RBF神经网络模型分别对铁路客货运量进行预测,并与传统的BP神经网络、RBF神经网络预测模型进行比较。结果显示,经过参数调整优化后的MI-PSO-RBF神经网络在铁路客运量及货运量的预测精度方面表现最佳,测试集R2分别达到了0.9481与0.9911,具有较高的精度及泛化能力,表明该组合预测模型能够进一步提升神经网络模型预测铁路客货运量精确度。

基于RBF神经网络的汽车内饰皮革智能切割系统设计

随着汽车用皮革的迅速发展,开发一套满足汽车内饰皮革生产需求的智能切割系统具有重要意义。本文简述了汽车内饰皮革切割系统的发展,构建了基于径向基函数(Radial Basis Function,RBF)神经网络的汽车内饰皮革智能切割系统,介绍了系统主要硬件配置选型和软件的设计,提出了基于RBF神经网络PID(Proportional Integral Derivative,比例-积分-微分)控制算法;通过搭建试验平台,测试汽车内饰皮革智能切割系统的可行性、切割精度与效率。结果表明,该系统能够较好地满足汽车内饰皮革切割方面的需求。

基于动态规划与RBF神经网络的PHEV能量管理策略

为提高插电式混合动力汽车燃油经济性,设计了一种基于动态规划和径向基函数(RBF)神经网络的插电式混合动力汽车能量管理策略。首先,建立了插电式混合汽车数学模型;其次,以发动机油耗最小为目标函数,采用动态规划求解全局最优的离线优化结果;最后,采用RBF神经网络对离线最优控制结果进行学习,建立了发动机输出转矩与车辆状态参数之间的非线性映射关系,得到了基于动态规划和RBF神经网络的能量管理策略。仿真结果表明,文章所提策略油耗较之于电量消耗-维持策略降低了2.92%,验证了该策略的有效性。

改进RBF神经网络在智能机器人轨迹规划中的研究

针对工业生产中对智能机器人轨迹规划的要求越来越高,在工业机器人运动模型的基础上,提出了一种将RBF神经网络和遗传算法相结合的工业机器人轨迹规划方法。通过遗传算法对RBF神经网络的网络结构、连接权值和阈值进行优化,精确跟踪机器人的轨迹。通过仿真将与未改进前的轨迹规划算法进行比较,验证该方法的优越性。结果表明,与改进前的规划算法相比,文中规划方法误差小,适应性强,能够满足工业机器人轨迹规划的预期要求。为工业机器人轨迹规划方法的发展提供了一定的参考。