A comparative study of metamodeling methods considering sample quality merits

This research focuses on the study of the relationships between sample data characteristics and metamodel performance considering different types of metamodeling methods. In this work, four types of metamodeling methods, including multivariate polynomial method, radial basis function method, kriging method and Bayesian neural network method, three sample quality merits, including sample size, uniformity and noise, and four performance evaluation measures considering accuracy, confidence, robustness and efficiency, are considered. Different from other comparative studies, quantitative measures, instead of qualitative ones, are used in this research to evaluate the characteristics of the sample data. In addition, the Bayesian neural network method, which is rarely used in metamodeling and has never been considered in comparative studies, is selected in this research as a metamodeling method and compared with other metamodeling methods. A simple guideline is also developed for selecting candidate metamodeling methods based on sample quality merits and performance requirements.     

Uncertain data stream algorithm based on clustering RBF neural network

In this paper, the existing algorithms for modeling uncertain data streams based on radial basis function neural networks have problems of low accuracy, weak stability and slow speed. A new clustering method for uncertain data streams is proposed. Radial basis function neural network of the algorithm. The algorithm firstly models the uncertain data stream, then combines the fuzzy theory and the neural network principle to obtain the radial basis function neural network, and then obtains the radial basis function neural network through the clustering algorithm of the regular tetrahedral uncertain vector. The central weight and width weights ultimately result in hidden layer output and output layer output results. The experimental results show that the proposed algorithm is an effective algorithm for modeling uncertain data streams using clustering radial basis function neural networks. It has higher precision, stability and speed than similar algorithms.     

新型广义径向基函数神经网络结构研究

提出了一种新型的广义径向基函数(RBF)神经网络,并研究了该网络的学习方法.不同于传统三层结构的RBF网络,广义RBF网络增加了基函数输出加权层,并在输出层采用超曲面去逼近任意的非线性曲面.实例仿真结果表明,与传统的RBF网络相比,该网络具有良好的逼近性能,收敛速度快,可逼近任意多变量非线性函数.     

Research on an online self-organizing radial basis function neural network

A new growing and pruning algorithm is proposed for radial basis function (RBF) neural network structure design in this paper, which is named as self-organizing RBF (SORBF). The structure of the RBF neural network is introduced in this paper first, and then the growing and pruning algorithm is used to design the structure of the RBF neural network automatically. The growing and pruning approach is based on the radius of the receptive field of the RBF nodes. Meanwhile, the parameters adjusting algorithms are proposed for the whole RBF neural network. The performance of the proposed method is evaluated through functions approximation and dynamic system identification. Then, the method is used to capture the biochemical oxygen demand (BOD) concentration in a wastewater treatment system. Experimental results show that the proposed method is efficient for network structure optimization, and it achieves better performance than some of the existing algorithms.     

一种改进PSO优化RBF神经网络的新方法

为了克服神经网络模型结构和参数难以设置的缺点,提出了一种改进粒子群优化的径向基函数(RBF)神经网络的新方法.首先将最近邻聚类用于RBF神经网络隐层中心向量的确定,同时对引入适应度值择优选取的原则对基本粒子群算法进行改进,采用改进粒子群(IMPSO)算法对最近邻聚类的聚类半径进行优化,合理的确定了RBF神经网络的隐层结构.将改进PSO优化的RBF神经网络应用于非线性函数逼近和混沌时间序列预测,经实验仿真验证.与基本粒子群(PSO)算法,收缩因子粒子群(CFA PSO)算法优化的RBF神经网络相比较,其在识别精度和收敛速度上都有了显著的提高.     

Prediction of reservoir sensitivity using RBF neural network with trainable radial basis function

Reservoir sensitivity prediction is an important basis for designing reservoir protection program scientifically and exploiting oil and gas resources efficiently. Researchers have long endeavored to establish a method to predict reservoir sensitivity, but all of the methods have some limitations. Radial basis function (RBF) neural network, which provided a powerful technique to model non-linear mapping and the learning algorithm for RBF neural networks, corresponds to the solution of a linear problem, therefore it is unnecessary to establish an accurate model or organize rules in large number, and it enjoys the advantages such as simple network structure, fast convergence rate, and strong approximation ability, etc. However, different radial basis function has different non-linear mapping ability, and different data require different radial basis functions. Nowadays, the choice of radial basis function in the network is based on experience or test result only, which exerts a great adverse impact on the network performance. In this study, a new RBF neural network with trainable radial basis function was proposed by the linear combination of common radial basis functions. The input parameters of the network were the influence factors of reservoir sensitivity such as porosity and permeability, etc. The output parameter was the corresponding sensitivity index. The network was trained and tested with the data collected from our own experiments. The results showed that the new RBF neural network is effective and improved, of which the accuracy is obviously higher than the network with single radial basis function for the prediction of reservoir sensitivity.     

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RBF神经网络观测器的非线性系统鲁棒故障检测方法

针对一类仿射非线性动态系统，提出一种基于网络非线性观测器的鲁棒故障检测与隔离的新方法，采用ＲＢＦ神经网络逼近观测器系统中的非线性项，提高了状态估计的精度，证明了状态估计误差稳定且渐近收敛到零；同时提出了一种新的网络权值调整指标方法，提高了神经网络故障分类器的泛化能力，从而保证该方法对监测系统的建模 外部扰动具有良好的鲁棒性。     

A clustering technique for digital communications channelequalization using radial basis function networks

The application of a radial basis function network to digital communications channel equalization is examined. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function network to realize the Bayesian equalization solution can be achieved efficiently using a simple and robust supervised clustering algorithm. During data transmission a decision-directed version of the clustering algorithm enables the radial basis function network to track a slowly time-varying environment. Moreover, the clustering scheme provides an automatic compensation for nonlinear channel and equipment distortion. Computer simulations are included to illustrate the analytical results.     

Power quality disturbance detection and classification using wavelet and RBFNN

This paper introduces a novel approach to detect and classify power quality disturbance in the power system using radial basis function neural network (RBFNN). The proposed method requires less number of features as compared to conventional approach for the identification. The feature extracted through the wavelet is trained by a radial basis function neural network for the classification of events. After training the neural network, the weight obtained is used to classify the Power Quality (PQ) problems. For the classification, 20 types of disturbances are taken into account. The classification performance of RBFNN is compared with feed forward multilayer network (FFML), learning vector quantization (LVQ), probabilistic neural network (PNN) and generalized regressive neural network (GRNN). The classification accuracy of the RBFNN network is improved, just by rewriting the weights and updating the weights with the help of cognitive as well as the social behavior of particles along with fitness value. The simulation results possess significant improvement over existing methods in signal detection and classification.     

Online identification using radial basis function neural network coupled with KPCA

In this paper, we are going to propose an online radial basis function (RBF) neural network algorithm without any preprocessing step. Then a kernel principal component analysis (KPCA) is coupled with the proposed online RBF neural network algorithm. Indeed, the KPCA method is used as a preprocessing step to reduce the feature dimension which fed to the RBF neural network. Reducing memory requirements of the models makes RBF neural network training efficient and fast. These two proposed algorithms are applied, with success, for identification of a mobile robot position. The simulation results present that the used sigmoid function as a kernel, compared to other kernel functions, which gives an excellent model and a minimum mean square error.     

一类基于神经网络非线性观测器的鲁棒故障检测

针对一类仿射非线性动态系统,提出了一种基 于神经网络非线性观测器的鲁棒故障检测与隔离的新方法．该方法采用神经网络逼近观测器 系统中的非线性项,提高了状态估计的精度,并从理论上证明了状态估计误差稳定且渐近收 敛到零;另一方面引入神经网络分类器进行故障的模式识别,通过在神经网络输入端加入噪 声项来进行训练,提高神经网络的泛化逼近能力,从而保证对被监测系统的建模误差和外部 扰动具有良好的鲁棒性．最后,利用本文方法针对某型歼击机结构故障进行仿真验证,仿真 结果表明本文方法是有效的．     

基于奇异值分解和径向基函数神经网络的人脸识别算法研究

提出一种基于奇异值分解和径向基函数神经网络的人脸特征提取与识别方法,来解决人脸识别中的高维、小样本问题。该方法采用奇异值分解、奇异值降维压缩、奇异值矢量标准化和奇异值矢量排序,最后得到用于识别的奇异值特征矢量。运用基于径向基函数神经网络分类器进行人脸分类识别。在ORL数据库上进行实验和数据分析表明,该方法无论是在分类的错误率上还是在学习的效率上都能表现出极好的性能。     

基于径向基函数神经网络的红外步态识别

为提高红外步态识别的效果,提出一种基于径向基函数神经网络的多分类器融合算法。对红外步态序列,分别应用基于轮廓线傅立叶描述子特征的模糊分类器和基于下肢关节角度特征的贝叶斯分类器进行识别,再利用径向基函数神经网络的学习和分类功能,对获得的输出信息进行度量层的融合和再识别。仿真实验结果表明,该算法获得更加精确的分类效果。     

一种应用ARPSO优化RBF神经网络的方法

针对径向基函数神经网络参数难以设置以及因此而导致的网络隐层结构不明朗的问题,提出了一种应用控制种群多样性的微粒群（ ARPSO）优化径向基函数神经网络（ RBF）的方法。通过引入“吸引”和“扩散”因子对基本微粒群算法进行改进,并将改进的微粒群算法用于RBF聚类半径的优化,进而能够合理地确定RBF的隐层结构。将用ARPSO优化的RBF神经网络应用于非线性函数逼近,经实验仿真验证,与基本微粒群（ PSO）算法、收缩因子微粒群（ CFA PSO）算法优化的RBF神经网络相比较,在收敛速度和识别精度上有了显著的提高。     

一种优化的RBF神经网络在调制识别中的应用

提出了一种基于径向基函数 (RBF) 神经网络的通信信号调制识别方法, 该方法采用模糊 C-均值 (FCM) 聚类算法对数据进行聚类, 并获取基函数的参数, 采用梯度下降法训练网络权值. 利用最优停止法对网络进行了优化, 避免了过学习现象, 提高了 RBF 网络的训练速度和泛化能力, 以实际信号数据对该网络进行性能检验, 实验结果表明了该 RBF 网络具有较高的识别精度.     

基于距离和密度聚类的RBF网络结构设计

为确定径向基函数RBF(radial basis function)神经网络隐含层结构,并针对基于距离或密度聚类的RBF神经网络的限制,提出一种基于距离和密度聚类(GDD)算法的RBF神经网络。GDD算法通过计算每个样本的密度,各样本间的距离及相似条件(密度标准、距离标准),相似条件是根据样本分布而改变的,进行样本空间划分,实现无需先验知识及参数,就可以精确识别任意形状的聚类。采用GDD算法聚类,RBF神经网络能确定合适的隐含层节点个数及径向基函数中心。对典型非线性函数逼近及UCI机器学习库实例样本进行实验,结果表明基于GDD算法设计的RBF神经网络具有良好的逼近能力和分类效果,且网络结构更加紧凑。     

基于径向基神经网络的蛋白质二级结构预测

为提高蛋白质二级结构预测的精确度,提出并构建精确的径向基神经网络、广义回归神经网络,并基于5位编码和Profile编码,采用不同大小的滑动窗口,利用交叉检证法构建多个径向基网络预测器,分别对蛋白质二级结构进行预测,得到了较好的实验结果,其中aveQ3提高到70.96%。结果表明,径向基神经网络模型能有效提高预测精确度,也证明了实验方法的有效性和可行性。     

Efficient clustering of radial basis perceptron neural network for pattern recognition

This paper studies how to train a new feed-forward neural network, radial basis perceptron (RBP) neural network, for distinguishing different sets in R^L. RBP neural network is based on radial basis function (RBF) neural network and perceptron neural network. It has two hidden layers where the nodes are not fully connected but use selective connection. A training algorithm corresponding to the structure of RBP network is presented. It adopts the input-output clustering (IOC) method to provide an efficient and powerful procedure for constructing a RBP network that generalizes very well. First, during the learning procedure, RBP neural network adopts IOC method to define the number of units of hidden layers and select centers. Second, the width parameter σ of centers is self-adjustable according to the information included in the learning samples. The effectiveness of this network is illustrated using an example taken from applications for component analysis of civil building materials. Simulation shows that RBP neural network can be used to predict the components of civil building materials successfully and gets good generalization ability.     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.