基于径向基函数(RBF)的安徽省GDP增长模拟与预测

本文运用新型非线性径向基函数RBF神经网络模型，对安徽省国内生产总值(GDP)进行了宏观经济模拟预测分析，结果证明与其它经济计量方法相比较，网络模型新颖，具有较好的预测精度及效果，可广泛应用于各种预测研究，有较高的应用推广价值。     

A new structure adaptation algorithm for RBF networks and its application

An adaptation algorithm is developed for radial basis function network (RBFN) in this paper. The RBFN is adapted on-line for both model structure and parameters with measurement data. When the RBFN is used to model a non-linear dynamic system, the structure is adapted to model abrupt change of system operating region, while the weights are adapted to model the incipient time varying parameters. Two new algorithms are proposed for adding new centres while the redundant centres are pruned, which is particularly useful for model-based control. The developed algorithm is evaluated by modelling a numerical example and a chemical reactor rig. The performance is compared with a non-adaptive model.     

So near and yet so far: New insight into properties of some well-known classifier paradigms

This article provides some new insight into the properties of four well-established classifier paradigms, namely support vector machines (SVM), classifiers based on mixture density models (CMM), fuzzy classifiers (FCL), and radial basis function neural networks (RBF). It will be shown that these classifiers can be formulated in a way such that they are functionally equivalent or at least highly similar. The interpretation of a specific classifier as being an SVM, CMM, FCL, or RBF then only depends on the objective function and the optimization algorithm used to adjust the parameters. The properties of these four paradigms, however, are very different: a discriminative classifier such as an SVM is expected to have optimal generalization capabilities on new data, a generative classifier such as a CMM also aims at modeling the processes from which the observed data originate, and a comprehensible classifier such as an FCL is intended to be parameterized and understood by human domain experts. We will discuss the advantages and disadvantages of these properties and show how they can be measured numerically in order to compare these classifiers. In such a way, the article aims at supporting a practitioner in assessing the properties of classifier paradigms and in selecting or combining certain paradigms for a given application problem.     

Speech recognition using pulse-coupled neural networks with a radial basis function

We have developed a novel pulse-coupled neural network (PCNN) for speech recognition. One of the advantages of the PCNN is in its biologically based neural dynamic structure using feedback connections. To recall the memorized pattern, a radial basis function (RBF) is incorporated into the proposed PCNN. Simulation results show that the PCNN with a RBF can be useful for phoneme recognition. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Fast evaluation of neural networks via confidence rating

Neural networks have become very useful tools for input–output knowledge discovery. However, some of the most powerful schemes require very complex machines and, thus, a large amount of calculation. This paper presents a general technique to reduce the computational burden associated with the operational phase of most neural networks that calculate their output as a weighted sum of terms, which comprises a wide variety of schemes, such as Multi-Net or Radial Basis Function networks. Basically, the idea consists on sequentially evaluating the sum terms, using a series of thresholds which are associated with the confidence that a partial output will coincide with the overall network classification criterion. Furthermore, we design some procedures for conveniently sorting out the network units, so that the most important ones are evaluated first. The possibilities of this strategy are illustrated with some experiments on a benchmark of binary classification problems, using RealAdaboost and RBF networks, which show that important computational savings can be achieved without significant degradation in terms of recognition accuracy.     

神经网络集成

神经网络集成通过训练多个神经网络并将成结论进行合成,可以显著地提高学习系统的泛化能力。它不仅有助于科学家对机器学习和神经的深入研究,还有助于普通工程技术人员利用神经网络技术来解决真实世界中的问题。因此,它被视为一种广阔应用前景的工程化神经计算技术,已经成为机器学习和神经计算领域的研究热点。该文从实现方法、理论分析和应用成果等三个方面综述了神经网络集成的国际研究现状,并对该领域值得进一步研究的一些问题进行了讨论。     

A Localized Forgetting Method for Gaussian RBFN Model Adaptation

In this paper a localized forgetting method is proposed for on-line adaptation of Gaussian radial basis function network models. It is realised that the commonly used exponential forgetting applies to the past data from the entire operating space uniformly and therefore, is not correct for nonlinear systems where dynamics are different in different operating regions. The new method proposed in this paper sets different forgetting factors in different regions according to the response of the local centre to the current measurement data. The method is applied in conjunction with the recursive orthogonal Least Squares algorithm and the computing is consequently very efficient. The developed method is applied to modelling of dissolved oxygen in a chemical reactor rig. It shows a smaller mean squared error for one-step-ahead prediction than using the uniform forgetting.     

基于RBF神经网络观测器飞控系统故障诊断

为了解决非线性系统采用解析方法进行故障诊断困难的问题,利用神经网络可逼近任意连续有界非线性函数的能力,提出了一种基于RBF神经网络观测器的故障检测与诊断方法,并详细论述了该故障诊断方法的构造原理。以含有非线性项的飞行控制系统的作动器模型为例,仅作动器的输入输出可测量,通过构造RBF神经网络观测器来拟合作动器系统模型,逼近其在正常情况下的输出。最后在飞控系统的闭环控制环境下,对作动器的三种典型故障进行了计算机仿真诊断,结果表明故障诊断方法是有效的。     

Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks

This paper introduces a new decentralized adaptive neural network controller for a class of large-scale nonlinear systems with unknown non-affine subsystems and unknown interconnections represented by nonlinear functions. A radial basis function neural network is used to represent the controller’s structure. The stability of the closed loop system is guaranteed through Lyapunov stability analysis. The effectiveness of the proposed decentralized adaptive controller is illustrated by considering two nonlinear systems: a two-inverted pendulum and a turbo generator. The simulation results verify the merits of the proposed controller.     

Prediction of chenille yarn and fabric abrasion resistance using radial basis function neural network models

The abrasion resistance of chenille yarn is crucially important in particular because the effect sought is always that of the velvety feel of the pile. Thus, various methods have been developed to predict chenille yarn and fabric abrasion properties. Statistical models yielded reasonably good abrasion resistance predictions. However, there is a lack of study that encompasses the scope for predicting the chenille yarn abrasion resistance with artificial neural network (ANN) models. This paper presents an intelligent modeling methodology based on ANNs for predicting the abrasion resistance of chenille yarns and fabrics. Constituent chenille yarn parameters like yarn count, pile length, twist level and pile yarn material type are used as inputs to the model. The intelligent method is based on a special kind of ANN, which uses radial basis functions as activation functions. The predictive power of the ANN model is compared with different statistical models. It is shown that the intelligent model improves prediction performance with respect to statistical models.     

基于模糊聚类的神经网络集成

针对差异性是集成学习的一个重要条件,研究基于模糊聚类技术提高神经网络集成差异性的方法。提取大量弱分类器的权值和阈值并作为模糊聚类的数据对象,然后将聚类结果作为集成网络中个体网络的权值和阈值,最后在标准数据集上进行仿真实验,证实方法的有效性。     

基于径向基函数(RBF)映射理论的遥感影像分类模型研究

与传统统计方法的分类器相比较,人工神经网络（ＡＮＮ）方法应用于遥感影像分类,不需预先假设样本空间的参数化统计分布,具有复杂的映射能力。大多数ＡＮＮ分类器采用误差反向传播（ＢＰ）学习算法的多层感知器模型（ＢＰＮＮ）,其主要缺陷是学习速度缓慢、容易陷入局部极小而导致难以收敛等。基于径向基函数（ＲＢＦ）映射理论的神经网络模型融合了参数化统计分布模型和非参经线性感知器映射模型的优点,在实现快速学习的同时,     

DE+RBFNs based classification: A special attention to removal of inconsistency and irrelevant features

A novel approach for the classification of both balanced and imbalanced dataset is developed in this paper by integrating the best attributes of radial basis function networks and differential evolution. In addition, a special attention is given to handle the problem of inconsistency and removal of irrelevant features. Removing data inconsistency and inputting optimal and relevant set of features to a radial basis function network may greatly enhance the network efficiency (in terms of accuracy), at the same time compact its size. We use Bayesian statistics for making the dataset consistent, information gain theory (a kind of filter approach) for reducing the features, and differential evolution for tuning center, spread and bias of radial basis function networks. The proposed approach is validated with a few benchmarked highly skewed and balanced dataset retrieved from University of California, Irvine (UCI) repository. Our experimental result demonstrates promising classification accuracy, when data inconsistency and feature selection are considered to design this classifier.     

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。     

Multiple models switching control based on recurrent neural networks

This paper presents a novel approach in designing adaptive controller to improve the transient performance for a class of nonlinear discrete-time systems under different operating modes. The proposed scheme consists of generalized minimum variance (GMV) controllers and a compensating controller. GMV controllers are based on the known nominal linear multiple models, while the compensating controller is based upon a recurrent neural network. The adaptation law of network weight is derived from Lyapunov stability theory. A suitable switching control strategy is applied to choose the best controller by the performance indices at every sampling instant. Simulations are discussed in order to illustrate the merits of the proposed method.     

混沌时间序列的混合粒子群优化预测

提出一种混合粒子群优化算法,即在改进粒子群优化算法全局搜索模型参数的基础上,利用梯度下降法进一步确定径向基神经网络模型参数,以提高网络的收敛精度和网络性能.采用基于RBFNN的混合粒子群优化算法进行离散Henon和连续Mackey-Glass混沌时间序列预测仿真,结果表明该算法能快速精确地预测混沌时间序列,是研究复杂非线性动力系统辨识和控制的一种有效方法.     

退化交通标志图像的RBPNN分类算法研究

为了识别退化的交通标志图像,采用模糊-仿射联合不变矩直接提取图像的特征,并针对各阶模糊-仿射联合不变矩数量级差异较大问题,提出一种数量级标准化算法,避免了需要较大计算量的图像复原处理过程。同时在深入研究径向基概率神经网络的基础上,采用全局K-均值算法优化其网络结构,并将其用于交通标志图像的分类识别。仿真结果表明,模糊-仿射联合不变矩是一种有效的处理退化交通标志图像的方法,所设计的径向基概率神经网络分类器不仅具有精简的结构而且有较好分类精度和推广性能。