A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm

Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.

基于微形变分析的电容式MEMS加速度计温漂误差精密估计方法

针对传统的电容式微机电系统(micro-electro-mechanical system,MEMS)加速度计(capacitive MEMS accelerometers,CMA)温漂误差(temperature drift error,TDE)补偿方法存在非精准溯源TDE致使TDE估计精度低、反复尝试估计模型构型导致构建过程复杂繁琐的问题,提出基于微形变分析的CMA TDE精密补偿方法。首先,通过微形变分析内部结构精准溯源TDE,基于径向基函数神经网络(radial basis function neural network,RBFNN)构建改进型TDE精密估计模型;其次,基于专家经验和模糊理论提出Expert-Fuzzy辅助决策下TDE估计模型辨识方法,为改进模型提供有效的构型指导;然后,设计升温试验测试CMA,构建传统模型和改进模型并通过对比其输出偏置稳定性评估TDE估计性能。实验结果表明,改进模型构建过程大大简化,补偿后CMA偏置稳定性提升约35%。本方法能够更加精准地估计TDE,有效解耦硅基材料的温度依赖性并提升CMA的环境适应性。

Analytic design of information granulation-based fuzzy radial basis function neural networks with the aid of multiobjective particle swarm optimization

Purpose–The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation(IG-FRBFNN)and their optimization realized by means of the Multiobjective Particle Swarm Optimization(MOPSO).Design/methodology/approach–In fuzzy modeling,complexity,interpretability(or simplicity)as well as accuracy of the obtained model are essential design criteria.Since the performance of the IG-RBFNN model is directly affected by some parameters,such as the fuzzification coefficient used in the FCM,the number of rules and the orders of the polynomials in the consequent parts of the rules,the authors carry out both structural as well as parametric optimization of the network.A multi-objective Particle Swarm Optimization using Crowding Distance(MOPSO-CD)as well as O/WLS learning-based optimization are exploited to carry out the structural and parametric optimization of the model,respectively,while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy.Findings–The performance of the proposed model is illustrated with the aid of three examples.The proposed optimization method leads to an accurate and highly interpretable fuzzy model.Originality/value–A MOPSO-CD as well as O/WLS learning-based optimization are exploited,respectively,to carry out the structural and parametric optimization of the model.As a result,the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.

基于平滑因子引入和神经网络优化的锂电池SOC估计方法

为提高锂电池荷电状态(SOC)的估计精度,提出了一种基于平滑因子引入和神经网络优化的锂电池SOC估计方法。将黄金分割优选法和模糊C均值聚类算法应用于RBF神经网络,分别用来确定最佳隐含层神经元个数和径向基中心;采用遗传算法对高斯核函数宽度及连接权值进行优化,解决了RBF神经网络结构和初始参数难以确定的问题。将滑动时间窗口内的放电容量作为平滑因子引入神经网络模型,增强了RBF网络对锂离子电池非线性特性拟合的能力。基于实验获得的锂离子电池在联邦城市行车计划(FUDS)工况下的数据,对所提出的方法进行仿真和验证,结果表明,所提方法显著提升了锂电池SOC的估计精度。

基于Fuzzy-RBFNN的多模医学图像融合方法研究

文中将模糊推理理论与径向基神经网络相结合构造了一个基于模糊推理的径向基神经网络(Fuzzy-RBFNN)应用于多模医学图像融合,并应用遗传算法训练网络获得网络参数,可自适应地完成多模医学图像融合。通过与基于梯度的金字塔融合方法的实验比较,证明了算法的有效性与可行性。

基于数字孪生的主动电网设备状态推演方法

主动电网的动态变化、网络异构和信息庞杂等特性给孪生体的高精准度映射带来了巨大挑战。为解决数字孪生主动电网的高精准度映射问题,提出了一种基于径向基函数神经网络的设备状态推演方法。方法通过建立径向基函数神经网络的电网设备状态监测器,获取正常负荷下的残差数据,并计算设备负荷系数。然后,利用基于马氏距离的相似矩阵识别设备的运行环境,并调用相应的设备推演模型,实现对复杂工况下设备状态的精确推演。实验证明,上述方法的设备状态推演准确率高达92%,满足数字孪生主动电网的要求,为电网智能调控和精确规划提供了基础信息,同时为电网数字化建设提供了理论参考。

Global Optimization Method Using SLE and Adaptive RBF Based on Fuzzy Clustering

High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.

Improving Land Resource Evaluation Using Fuzzy Neural Network Ensembles

陆地评估因素经常包含连续 -- ，分离值、名字值的属性。在传统的陆地评估，这些不同属性通常由陆路被分级进范畴的索引资源专家，和评估结果重重地依靠专家的经验。以便克服缺点，我们介绍了没要求 grading 的一个模糊神经网络整体方法评估因素进范畴的索引并且能由直接使用三种属性价值评估土地资源。模糊的背繁殖神经网络(BPNN ) ，一个模糊光线的基础函数神经网络(RBFNN ) ，一个模糊 BPNN 整体，和一个模糊 RBFNN 整体被用来在广东评估土地资源省。由使用模糊 BPNN 整体和模糊 RBFNN 整体的评估结果是比由使用单个模糊 BPNN 和单个模糊 RBFNN 的那些好一些的，并且单个模糊 RBFNN 或模糊 RBFNN 整体的错误率分别地比单个模糊 BPNN 或模糊 BPNN 整体的低。由使用模糊神经网络整体，土地资源评估的有效性被改进，陆地计算程序的经验上的信赖更加被减少。

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller （NFC） in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.

Shape identification of electrocardiographic ST segment based on radial basis function neural network

The types of myocardial ischemia can be revealed by electrocardiographic(ECG)ST segment.Effective mea-surement and electrocardiographic analysis of ST as well as calculation of displacement and shape change of ST segment can help doctors diagnose coronary heart disease and myocar-dial ischemia,especially for asymptomatic myocardial isch-emia.Therefore,it is a very important subject in clinical practice to measure and classify the ECG ST segment.In this paper,we introduce a computerized automatic identification method of the electrocardiographic ST segment shape with radial basis function neural network based on adaptive fuzzy system,which has a better effect than other methods.It helps to analyze the reason of the ST segment change and confirm the position of myocardial ischemia,and is useful for doctor diagnosis.

融合FCM-RBF的短时交通拥堵状态预测模型

针对高速公路常发性拥堵路段,提出一种融合模糊C均值聚类算法和径向基函数神经网络的短时交通拥堵状态预测模型。模型基于FCM聚类算法获取历史交通流的拥堵状态标签以及不同交通状态的聚类中心;基于RBF神经网络算法实现短时交通流参数预测。将RBF神经网络预测得到的短时交通流参数代入FCM聚类结果中,得到短时交通拥堵状态标签。通过交通流参数与交通状态的隐含关系,搭建出融合模型的基本计算架构。结果表明:FCM聚类算法训练后的分类结果更加稳定有效;RBF神经网络比对照方法具有更高的预测精度,预测相对误差基本低于1.2%;建立的FCM-RBF模型对短时交通拥堵状态预测的分类正确率达到95%,预测结果准确可靠。

考虑能见度影响的公路隧道照明动态优化与智能控制

为解决不同能见度影响下公路隧道实际路面亮度变化过大以及由此引起的行车安全与能源虚耗问题,本文提出了一种能够改善公路隧道照明环境的动态优化与智能控制方法.首先,通过对不同时空条件下的公路隧道进行现场试验和数据分析,得到了隧道内能见度的变化规律;其次,在公路隧道传统照明设计的基础上考虑能见度对照明环境的影响,建立了基于隧道内能见度、交通量、车速、路面亮度和照明亮度的按需照明与动态优化模型;随后,以不同地区公路隧道的实测数据为样本,结合划分出的公路隧道典型照明场景和模糊径向基神经网络算法构建了公路隧道照明智能控制模型,最后,通过仿真实验验证了所构建模型的有效性,其结果表明,本文所提出的优化控制方法能够在保证隧道照明安全性的前提下兼顾节能性.

Neural-networks-based Modelling and a Fuzzy Neural Networks Controller of MCFC

Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.

失效模式的模糊风险评估及其评估参数的灵敏度分析

目前失效模式风险评估研究主要集中在给出更为复杂的评估模型,而缺乏对评估模型的工程应用考虑和各评估参数对评估结果影响性质和影响程度的研究。本文首先研究构建失效模式风险因子的模糊置信区间评估模型,通过将模糊置信区间数转化为三角模糊数,实现风险因子评估结果的综合及评估结果的标量度量;以此为基础分析给出风险因子评估模型中各评估参数的概率分布模型,再应用基于高斯径向基函数的全局灵敏度分析方法,确定各评估参数对评估结果的影响性质和影响程度,据此指导制定更为精细的评估参数取值准则。某型飞机发动机安装工艺过程的失效模式及影响分析(FMEA)应用研究结果表明,所给出的模型和基于灵敏度分析结果制定的评估参数取值准则可使评估专家更为客观精细地给出风险因子的评估意见,得到满足工业标准要求的风险评估结果,并显著提升评估结果对失效模式风险的区分度。

考虑加速度频响函数不确定的有限元模型修正

为克服实际应用中缺乏足够的结构统计信息,获得结构参数和响应的极限值,提出了一种基于加速度频响函数的区间有限元模型修正方法。首先,将频响函数小波变换,提取低频小波系数作为模型修正的响应特征量,以待修正参数和响应特征量分别为输入和输出构建径向基代理模型并采用鲸鱼优化算法来优选径向基模型方差值;其次,根据区间重叠率和巴氏距离分别构造两步求解待修正参数区间的两个目标函数和同步求解待修正参数区间的一个目标函数,以评估两个样本区间分布的相似性和相异性;然后,由灰色数学方法估计径向基模型预测响应特征量的区间,运用花朵授粉算法分别实施待修正参数区间中点和半径的两步和同步求解;最后,通过两个数值算例和一个试验算例验证了所提方法的可行性。结果表明,所提区间有限元模型修正方法能够有效地修正结构参数的区间中点和半径,且在不同试验响应区间下对参数区间的修正具有鲁棒性,同时可以有效地解决小样本的不确定性模型修正问题。

基于CO-需风量理论的隧道施工通风控制优化模型研究

为保证施工通风质量、控制施工通风成本、降低碳排放、解决智能控制模型对隧道施工通风系统的适用性问题,首先,采用文献调研和理论分析结合的方法建立基于CO体积分数的爆破工况和出渣工况下隧道施工通风需风量理论模型;其次,设计应用于隧道施工通风系统的PID控制模型、模糊PID(Fuzzy PID)控制模型和RBF神经网络PID控制模型;最后,采用数值仿真的方法,基于MATLAB对各控制模型应用于隧道施工通风系统理论模型的参数确定和控制效果进行对比研究。结果表明:在隧道施工通风系统中,控制模型性能和抗干扰性有一定的负相关性,PID控制模型由于参数调节的便捷性使其在理论响应性能上较为优异,但Fuzzy PID控制模型或RBF-PID控制模型可更好地响应实际工程对于抗干扰和性能的需求。

智能电网电量异常数据的识别和修复研究

为了提升智能电网电量数据质量、保证电网安全运行,对智能电网电量异常数据的识别和修复进行了研究。创新性地采用粒子群优化(PSO)算法优化可能性模糊C均值(PFCM)聚类算法,组成PSO-PFCM算法。通过指标函数确定最优聚类数目和最优聚类中心、聚类电量特征曲线,结合各类别电量曲线特征识别异常电量数据。建立径向基函数(RBF)神经网络修复模型,输入识别到的异常电量样本数据。经归一化预处理、神经网络训练后,用输出结果替换异常电量值,以实现异常电量数据的修复。试验结果表明:该算法的异常电量数据检测率高于0.82、误检率低于0.06,修复后电量值更接近实际值,异常修复相对误差低于20%。以上数据证明所提算法可智能识别和修复异常电量数据,保证电网安全运行。

基于遗传算法—模糊径向基神经网络的光伏发电功率预测模型

针对光伏发电系统出力波动问题,提出遗传算法(GA)—模糊径向基(RBF)神经网络的光伏发电功率预测模型,将功率预测值应用于光伏发电的蓄电池储能功率调节系统,以降低对电网的冲击。选择与待预测日天气类型相同、日期相近、温度欧氏距离最小的历史日作为相似日,把与光伏发电功率相关性大的太阳辐射强度和温度作为模型输入变量,提出K均值聚类和遗传算法的参数优化方法,建立基于GA—模糊RBF神经网络的最终预测模型。在光伏功率预测的基础上,提出一种平滑控制策略,对光伏并网功率进行有效调节,从而达到平滑光伏功率波动的目的。实例证明,所述预测模型具有较高精度,并验证了平滑功率波动控制策略的有效性。

基于径向基函数神经网络的电网模糊元胞故障诊断

提出了基于径向基函数神经网络的电网模糊元胞故障诊断方法,旨在有效解决神经网络应用于电网故障诊断所面临的适应网络拓扑结构变化的可移植性问题。该方法以单个线路、母线和变压器为元胞对象,以保护各元胞的所有关联保护和对应的断路器为输入,建立了元胞通用神经网络诊断模型,并给出了故障诊断时模型的自动生成方法。此外,考虑到电网故障信息存在不完备性和不确定性,本文采用模糊矢状图来描述电网元件、保护和断路器之间的逻辑推理关系,并提取出蕴含不确定性的模糊推理规则,用于训练元胞通用神经网络。算例仿真结果表明,该方法简单、有效,能处理各种复杂故障情况,且能有效适应网络拓扑结构的变化,具有良好的容错性和可移植性。

基于神经网络的雷达抗转发式距离欺骗干扰方法

为实现雷达抗转发距离欺骗干扰,提出了一种基于神经网络和模式识别技术的目标与干扰的识别方法。该方法将抗干扰过程看作对目标和干扰进行识别的过程,利用干扰信号和目标的能量差别与起伏特性差异,确定出用于分类的特征因子,使该特征因子受信干比的影响远小于用传统检测方法时的影响,最后利用径向基(radialbasisfunction,RBF)神经网络进行分类。仿真结果表明,此方法在检测概率和稳定性上都优于传统方法,在信干比接近于零的情况下尤为明显。