基于RBF神经网络的飞机发动机故障诊断研究

论述了径向基函数神经网络的基本网络结构和网络的学习及运行过程,结果表明:径向基神经网络具有极快的学习收敛速度。讨论了径向基神经网络在飞机发动机故障诊断中的应用,并对训练后的网络进行了仿真测试,仿真结果表明RBF网络有较高诊断正确率,且能满足实时诊断的要求。     

人工神经网络在函数逼近中的应用研究

利用径向基函数网络研究了人工神经网络在函数逼近中的应用。分析了网络结构对逼近性能的影响。利用MATLAB神经网络工具箱进行仿真。实验结果表明,神经网络具有很好的函数逼近性能,其中RBF网络的逼近性能更优。     

一种改进的RBF网络结构及其参数的设计方法

针对神经网络在学习之后,模糊系统的原始结构被改变,或削弱了规则可解释性这一模糊系统突出特点的问题,给出了一种提取模糊If-then规则的径向基函数(RBF)神经网络结构。该神经网络结构具有能够同时清晰表达模糊控制系统输入空间划分和模糊规则可解释性的特点,克服了以往用神经网络提取模糊规则不能直观体现模糊语言规则可解释性的不足,并详细地讨论了此网络结构参数的设计方法。     

基于联合神经网络的传感器故障诊断与重构

针对可能发生的传感器故障,设计出了一种基于联合神经网络的传感器容错系统.提出了一种改进型的径向基函数神经网络,有较强的容错能力.算法包括1个主网络和n个分散网络的联合神经网络结构,各神经网络均基于改进型径向基函数算法,根据一定的控制目标对系统中的传感器故障进行检测、识别和调节,达到了容错控制的目的.     

一种基于RBF网络提取模糊规则的算法实现

径向基函数网络和模糊推理系统在一些柔和的情况下具有等价的功能,因此可以利用神经网络的学习算法来调节模糊系统的参数,学习后的模糊系统具有自学习和自组织性,但是削弱了模糊系统的可解释性。将模糊逻辑推理与神经网络控制技术相结合,分析了一种改进的径向基函数(RBF)神经网络结构,这种模糊神经网络结构能够有效地表达模糊系统可解释性这一突出特点,也使模糊系统具有了较好的自学习和自组织能力、通过VC 实现了基于这种RBF网络结构提取模糊规则的算法,并进行了仿真实验,仿真结果表明该算法是比较有效的。     

径向函数网络对国民经济生产总值预测研究

为了建立国民经济生产总值（GDP）神经网络预测模型,构造了双层网络结构的基于径向基网络,通过学习训练,确定径向基数神经网络参数和结构。仿真结果表明,生成的径向基函数模型应用于国民经济预测比BP神经网络模型具有更高的预测精度和良好的泛化能力。     

遗传算法自适应模糊神经网络控制

神经网络能够以任意精度逼近任意复杂的非线性关系,具有高度的自适应和自组织性,在解决高度非线性和严重不确定系统的控制方面具有巨大的潜力.但一般神经网络训练算法如BP算法训练速度慢,受初值影响大且易陷入局部极小点,该文提出了一种基于模糊神经网络的间接自校正控制系统,控制器以高斯隶属度函数的径向基函数（RBF）神经网络结构,利用改进的遗传算法（GA）对结构和参数进行同步优化,改进适应度函数指导搜索过程,在保证稳定情况下大大加快了收敛的速度.神经网络正向模型（NNP）利用弹性BP算法进行离线辨识,使得到的模型泛化性能好.     

RBF神经网络的结构动态优化设计

针对径向基函数(Radial basis function, RBF)神经网络的结构设计问题, 提出一种结构动态优化设计方法. 利用敏感度法(Sensitivity analysis, SA)分析隐含层神经元的输出加权值对神经网络输出的影响, 以此判断增加或删除RBF神经网络隐含层中的神经元, 解决了RBF神经网络结构过大或过小的问题, 并给出了神经网络结构动态变化过程中收敛性证明; 利用梯度下降的参数修正算法保证了最终RBF网络的精度, 实现了神经网络的结构和参数自校正. 通过对非线性函数的逼近与污水处理过程中关键参数的建模结果, 证明了该动态RBF具有良好的自适应能力和逼近能力, 尤其是在泛化能力、最终网络结构等方面较之最小资源神经网络(Minimal resource allocation networks, MRAN)与增长和修剪RBF 神经网络(Generalized growing and pruning radial basis function, GGAP-RBF) 有较大提高.     

基于改进的RBF神经网络在线辨识算法及其应用

针对径向基函数(RBF)神经网络用于非线性系统辨识时存在的问题，对径向基函数网络的拓扑结构作了改进，并给出了改进的径向基函数(MRBF)神经网络的中心选取方法和权值在线调整算法，最后用改进的径向基函数网络对一个典型工业对象(CSTR)进行了应用研究，结果表明方法有效。     

基于混沌映射的非径向对称基函数的神经网络模型

为提高神经网络模型的预测精度,构建了非径向对称基函数神经网络模型结构。为确定非径向对称基函数神经网络模型参数,采用Ulam-von Neumann映射规则确定混沌变量,利用混沌变量的遍历性获得不同网络结构参数下的最优网络输出,以减少所构建网络模型的实际输出与期望输出的差值,并利用模型输出的误差变化率以决定是否增加新的隐层节点。给出基于混沌映射的非径向对称基函数的网络模型构建步骤。采用基于Mackey-Glass时滞微分方程的混沌时间序列预测问题验证该模型的预测精度,并同其他文献对该序列预测的精度以及所需隐层节点数作对比。比较结果表明,采用该设计模型具有对时间序列预测精度高且所需网络结构规模小等优点。     

基于RBF神经网络的电机故障诊断的研究

在对国内外感应电动机故障诊断技术发展与研究的基础上,提出了从定子电流人手,利用径向基（RBF）神经网络算法来监测感应电动机工作状态,从而实现对电动机较为常见的电气故障和机械故障的综合检测。Matlab仿真结果表明RBF算法有效地实现了对电机故障诊断的研究。     

无线传感器网络的系统化自适应建模

无线传感器网络的系统能耗制约着全网络的综合应用能力,其中节点有限的能量从根本上影响着传感器网络效能。针对无线传感器网络的全局能耗问题,提出了基于径向基函数神经网络以及状态空间表达的系统化建模方法。考虑到无线传感器网络的拓扑结构与分级关系, 采用径向基函数神经网络自适应实时规划系统。鉴于各传感器节点对数据的不同处理方式与能耗密切相关, 对全系统能耗建立系统化矩阵模型。仿真分析表明该模型可根据实际应用背景调整设置完成全局优化。     

基于改进遗传算法的RBF神经网络结构优化研究

针对RBF神经网络隐含层节点数过多导致网络结构复杂的问题,提出了一种基于改进遗传算法(IGA)的RBF神经网络优化算法。利用IGA优化基于正交最小二乘法的RBF神经网络结构,通过对隐含层输出矩阵的列向量进行全局寻优,从而设计出结构更优的基于IGA的RBF神经网络(IGA-RBF)。将IGA-RBF神经网络的学习算法应用于电子元器件贮存环境温湿度预测模型,与基于正交最小二乘法的RBF神经网络进行比较的结果表明:IGA-RBF神经网络设计出来的网络训练步数减少了44步,隐含层节点数减少了34个,且预测模型得到的温湿度误差较小,拟合精度大于0.95,具有更高的预测精度。     

A review of genetic algorithms applied to training radial basis function networks

The problems associated with training feedforward artificial neural networks (ANNs) such as the multilayer perceptron (MLP) network and radial basis function (RBF) network have been well documented. The solutions to these problems have inspired a considerable amount of research, one particular area being the application of evolutionary search algorithms such as the genetic algorithm (GA). To date, the vast majority of GA solutions have been aimed at the MLP network. This paper begins with a brief overview of feedforward ANNs and GAs followed by a review of the current state of research in applying evolutionary techniques to training RBF networks.     

基于自适应扰动观测器的自主船舶协同路径跟踪控制

为实现未知环境扰动下不确定欠驱动自主船舶的协同路径跟踪控制, 本文提出了一种基于自适应扰动观 测器的鲁棒控制算法. 该算法采用径向基函数神经网络(RBFNNs)逼近模型参数不确定, 并利用最小学习参数化 (MLP)技术对神经网络的权重及逼近误差进行压缩, 所设计观测器不需要环境扰动上界的精确信息. 进一步, 基于 代数图论对船间通信进行建模, 设计了一种分散式协同控制律, 有效地降低了通信负载. 凭借Lyapunov稳定性理论 证明了闭环系统内信号的有界性, 且能通过对设计参数的调节使跟踪误差的收敛界为任意小. 最后采用数值仿真 试验验证了所提出算法的有效性和优越性.     

Combined projection and kernel basis functions for classification in evolutionary neural networks

This paper proposes a hybrid neural network model using a possible combination of different transfer projection functions (sigmoidal unit, SU, product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. An evolutionary algorithm is adapted to this model and applied for learning the architecture, weights and node typology. Three different combined basis function models are proposed with all the different pairs that can be obtained with SU, PU and RBF nodes: product–sigmoidal unit (PSU) neural networks, product–radial basis function (PRBF) neural networks, and sigmoidal–radial basis function (SRBF) neural networks; and these are compared to the corresponding pure models: product unit neural network (PUNN), multilayer perceptron (MLP) and the RBF neural network. The proposals are tested using ten benchmark classification problems from well known machine learning problems. Combined functions using projection and kernel functions are found to be better than pure basis functions for the task of classification in several datasets.     

Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet

To realize effective modeling and secure accurate prediction abilities of models for power supply for high-field magnet (PSHFM), we develop a comprehensive design methodology of information granule-oriented radial basis function (RBF) neural networks. The proposed network comes with a collection of radial basis functions, which are structurally as well as parametrically optimized with the aid of information granulation and genetic algorithm. The structure of the information granule-oriented RBF neural networks invokes two types of clustering methods such as K-Means and fuzzy C-Means (FCM). The taxonomy of the resulting information granules relates to the format of the activation functions of the receptive fields used in RBF neural networks. The optimization of the network deals with a number of essential parameters as well as the underlying learning mechanisms (e.g., the width of the Gaussian function, the numbers of nodes in the hidden layer, and a fuzzification coefficient used in the FCM method). During the identification process, we are guided by a weighted objective function (performance index) in which a weight factor is introduced to achieve a sound balance between approximation and generalization capabilities of the resulting model. The proposed model is applied to modeling power supply for high-field magnet where the model is developed in the presence of a limited dataset (where the small size of the data is implied by high costs of acquiring data) as well as strong nonlinear characteristics of the underlying phenomenon. The obtained experimental results show that the proposed network exhibits high accuracy and generalization capabilities.     

双光路光纤位移传感器的研究

提出了一种可消除温度对光纤位移传感器影响的双光路传感器结构,用径向基函数(RBF)神经网络对传感器输出进行了处理,并用实测数据对其进行了验证.结果表明:采用双光路结构,并结合RBF神经网络使传感器的热灵敏度漂移降低了近1个数量级.     

Identification of non-linear processes using reciprocal multiquadric functions

In this paper radial basis function (RBF) networks are used to model general non-linear discrete-time systems. In particular, reciprocal multiquadric functions are used as activation functions for the RBF networks. A stepwise regression algorithm based on orthogonalization and a series of statistical tests is employed for designing and training of the network. The identification method yields non-linear models, which are stable and linear in the model parameters. The advantages of the proposed method compared to other radial basis function methods and backpropagation neural networks are described. Finally, the effectiveness of the identification method is demonstrated by the identification of two non-linear chemical processes, a simulated continuous stirred tank reactor and an experimental pH neutralization process.     

An efficient multilayered shielded microwave circuit analysis method based on neural networks

In previous works, a neural network based technique to analyze multilayered shielded microwave circuits was developed. The method is based on the approximation of the shielded media Green's functions by radial‐basis‐function neural networks (RBFNNs). The trained neural networks, substitute the original Green's functions during the application of the integral equation approach, allowing a faster analysis than the direct solution. In this article, new and important improvements are applied to the training of the RBFNNs, which permit a reduction in the approximation error introduced by the neural networks. Furthermore, outstanding time reductions in the analysis of printed circuits are achieved, clearly outperforming the former technique. The main improvement consists on a better processing of the Green's function singularity near the source. The singularity produces rapid variations near the source that makes difficult the neural network training. In this work, the singularity is extracted in a more suitable fashion than in previous works. The functions resulting from the singularity extraction present a smooth behavior, so they can be easily approximated by neural networks. In addition, a new subdivision strategy for the input space is proposed to efficiently train the neural networks. Two practical microwave filters are analyzed using the new techniques. Comparisons with measured results are also presented for validation. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.