基于粒子群算法的RBF网络参数优化算法

针对神经网络的一些缺陷,研究神经网络基于粒子群优化的学习算法,将粒子群优化算法用于RBF神经网络的学习训练。提出了一种基于粒子群优化（PSO）算法的径向基（RBF）网络参数优化算法,首先利用减聚类算法确定网络径向基函数中心的个数,再用PSO算法优化径向基函数的中心及宽度,最后用PSO算法训练隐含层到输出层的网络权值,找到神经网络权值的最优解,以达到优化神经网络学习的目的。最后,通过一个实验与最小二乘法优化的神经网络进行了比较,验证了算法的有效性。     

基于QPSO—RBF NN的混沌时间序列预测*

提出一种基于量子粒子群优化算法训练径向基函数神经网络进行混沌时间序列预测的新方法.在确定径向基函数网络的隐层节点数后,将相应网络的参数,包括隐层基函数中心、扩展常数,以及输出权值和偏移编码成学习算法中的粒子个体,在全局空间中搜索具有最优适应值的参数向量.实例仿真证实了该方法的有效性.     

径向基函数神经网络的一种两级学习方法

建立RBF(radial basis function)神经网络模型关键在于确定网络隐中心向量、基宽度参数和隐节点数.为设计结构简单,且具有良好泛化性能径向基网络结构,本文提出了一种RBF网络的两级学习新设计方法.该方法在下级由正则化正交最小二乘法与D-最优试验设计结合算法自动构建结构节俭的RBF网络模型;在上级通过粒子群优化算法优选结合算法中影响网络泛化性能的3个学习参数,即基宽度参数、正则化系数和D-最优代价系数的最佳参数组合.仿真实例表明了该方法的有效性.     

基于广义径向基函数的神经网络分类预测

径向基函数网络是神经网络中一种广泛使用的设计方法.它把神经网络的设计看作是一个高维空间的曲线逼近问题.相对于其他的神经网络方法.径向基函数神经网络除了具有一般神经网络的优点,如多维非线性映射能力、泛化能力、并行信息处理能力等,还具有很强的聚类分析能力,学习算法简单方便等优点.针对一个实际分类问题,利用广义径向基函数网络的思想训练一个网络并实现对测试数据集的分类预测.本算法采用k-均值聚类算法训练广义径向基函数网络中心,使用奇异值分解计算输出层权值.对该网络的实现细节及待改进之处进行简要分析.实验表明广义径向基函数神经网络的思想具有很强的聚类分析能力,学习算法简单方便等优点.     

基于RBF神经网络PID控制的交流伺服系统

将神经网络和PID控制相结合,提出了一种神经网络整定的PID控制策略,并将其应用于交流伺服系统的控制.利用一个两层神经网络在线自适应调整PID控制器的参数,从而使系统的静态和动态性能指标较为理想.径向基函数神经网络用来辨识交流伺服系统的Jacobian信息,其学习算法采用正交最小二乘算法,首先得到径向基函数神经网络的结构.然后用BP算法对该网络的权值进行训练使它逼近给定的函数.实验结果表明,该交流伺服系统具有响应速度快、稳态精度高和鲁棒性强等特点.     

基于径向基函数神经网络的机器人系统辨识

介绍了一种三层径向基函数神经网络,其学习算法采用正交最小二乘算法.首先根据正交最小二乘算法得到径向基函数神经网络的结构;然后对该网络的权值进行训练使它逼近给定的函数.为了验证径向基函数神经网络所具有的对任意非线性映射的任意逼近能力和自学习、自适应能力,以两关节机械手为辨识对象来进行实验研究.实验结果表明,该径向基函数神经网络具有良好的模型学习和逼近能力,并且学习速度快、收敛性好、鲁棒性强,尤其适合于具有连续线性与非线性对象的复杂系统的控制实时性要求.     

基于混合递阶遗传算法的径向基神经网络学习算法及其应用

在研究径向基神经网络学习算法的基础上, 提出了一种新型的径向基神经网络学习算法———混合递阶遗传算法. 该算法将递阶遗传算法和最小二乘法的优点结合在一起, 能够同时确定径向基神经网络的结构和参数, 并具有较高的学习效率. 采用基于混合递阶遗传算法的径向基神经网络对混沌时间序列学习和预测, 取得了较好的效果.     

基于神经网络改进算法的飞控系统故障诊断

应用神经网络技术对复杂的飞行控制系统进行故障诊断对提高飞机的可靠性和容错能力具有重要意义。为了提高网络的学习效率和稳定性,该文提出一种改进的径向基神经网络学习算法,使用混合共轭梯度优化算法对网络参数进行调整。利用神经网络对某型飞机的飞行控制系统进行故障诊断,仿真结果表明该神经网络具有较强的故障识别能力。     

椭球基函数神经网络的混合学习算法

提出一种训练椭球基函数神经网络(EBFNN)的混合学习算法.此算法首先使用期望最大化算法初始化EBFNN中椭球基函数节点的参数,而网络的连接权重和偏差项则用线性最小二乘方法进行初始化.然后用梯度下降法对EBFNN中所有参数同时进行优化.与其他3个相关的模型相比,用混合学习方法训练的梯度下降椭球基函数神经网络(GDEBFNN)能够取得更优的分类性能.此外,与支持向量机对比表明,GDEBFNN取得与之接近的泛化能力.与基于Adaboost的决策树模型比较表明,GDEBFNN可以取得更优的泛化性能.     

径向基函数神经网络的一种构造算法

提出了径向基函数(RBF)神经网络参数的一种新的学习算法——分类优化迭代算法。在此基础上,设计了RBF网络的一种构造算法。仿真结果表明了本文方法的有效性。     

基于函数正交基展开的过程神经网络学习算法

过程神经网络的输入和连接权均可为时变函数，过程神经元增加了一个对于时间的聚合算子，使网络同时具有时空二维信息处理能力．该文在考虑过程神经网络对时间聚合运算的复杂性的基础上，提出了一种基于函数正交基展开的学习算法．在网络输入函数空间中选择一组适当的函数正交基，将输入函数和网络权函数都表示为该组正交基的展开形式，利用基函数的正交性．简化过程神经元对时间的聚合运算．应用表明，算法简化了过程神经网络的计算复杂度，提高了网络学习效率和对实际问题求解的适应性．以旋转机械故障诊断问题和油藏开发过程采收率的模拟为例验证了算法的有效性．     

基于复合正交神经网络的自适应逆控制系统

目前，在自适应逆控制系统中常采用BP神经网络，而BP网络存在算法复杂、易陷入局部极小解等不足。而正交神经网络能克服BP网络的不足，但由于正交神经网络学习算法存在某些局限性，提出了一种复合正交神经网络，该正交网络结构与三层前向正交网络相同，不同的是正交网络的隐单元处理函数采用带参数的Sigmoid函数的复合正交函数，该神经网络算法简单，学习收敛速度快，并能对网络的函数参数进行优化，为非线性系统的动态建模提供了一种方法。仿真实验表明，网络在用于过程的自适应逆控制中具有很高的控制精度和自适应学习能力。该动态神经网络比其它神经网络具有更强的建模能力与学习适应性，有线性、非线性逼近精度高等优异特性，非常适合于实时控制系统。     

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.     

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.     

分式过程神经元网络在网络流量预测中的应用

为更好解决网络流量预测问题,依据函数逼近论中分式的函数逼近性质和拟合能力要远远大于线性函数的性质,以及过程神经元网络对时变函数的非线性变换能力,提出一种分式过程神经元网络模型及其学习算法。实验结果证明,该网络模型对具有奇异值过程函数的柔韧逼近性质和在奇异值点附近区域反应的灵敏性优于一般过程神经元网络,以网络实测数据对模型进行训练和流量预测,取得了较好的应用效果。     

基于变学习率三角基函数神经网络的4型FIR滤波器设计

本文提出一种基于变学习率三角基函数神经网络的线性相位4型FIR滤波器设计方法。该方法根据三角基函数神经网络与线性相位4型FIR滤波器幅频特性之间的关系,构建了一种变学习率三角基函数神经网络模型,在神经网络训练过程中引入变学习率算法自调整学习率取值,解决学习率通常依靠经验或试凑法确定带来的不确定性,提高神经网络的学习效率和收敛速度。通过训练神经网络的权值,使设计的FIR滤波器幅频响应与理想幅频响应在整个通带和阻带内的误差平方和最小。文中利用该方法对FIR高通滤波器和带通滤波器进行了优化设计,仿真结果表明了该方法设计FIR滤波器的有效性和优越性。     

Learning Perceptrons and Piecewise Linear Classifiers Sensitive to Example Dependent Costs

This article considers the cost dependent construction of linear and piecewise linear classifiers. Classical learning algorithms from the fields of artificial neural networks and machine learning consider either no costs at all or allow only costs that depend on the classes of the examples that are used for learning. In contrast to class dependent costs, we consider costs that are example, i.e. feature and class dependent. We present a cost sensitive extension of a modified version of the well-known perceptron algorithm that can also be applied in cases, where the classes are linearly non-separable. We also present an extended version of the hybrid learning algorithm DIPOL, that can be applied in the case of linear non-separability, multi-modal class distributions, and multi-class learning problems. We show that the consideration of example dependent costs is a true extension of class dependent costs. The approach is general and can be extended to other neural network architectures like multi-layer perceptrons and radial basis function networks.     

Growing radial basis neural networks: merging supervised andunsupervised learning with network growth techniques

This paper proposes a framework for constructing and training radial basis function (RBF) neural networks. The proposed growing radial basis function (GRBF) network begins with a small number of prototypes, which determine the locations of radial basis functions. In the process of training, the GRBF network gross by splitting one of the prototypes at each growing cycle. Two splitting criteria are proposed to determine which prototype to split in each growing cycle. The proposed hybrid learning scheme provides a framework for incorporating existing algorithms in the training of GRBF networks. These include unsupervised algorithms for clustering and learning vector quantization, as well as learning algorithms for training single-layer linear neural networks. A supervised learning scheme based on the minimization of the localized class-conditional variance is also proposed and tested. GRBF neural networks are evaluated and tested on a variety of data sets with very satisfactory results.     

BP算法的改进及用模拟电路实现的神经网络分类器

基于用模拟电路实现神经网络分类器的目的，对多层静态前馈神经网络的ＢＰ算法做了改进，采用线性限幅函数代替Ｓｉｇｍｏｉｄ函数作为神经元的激活函数，给出了改进的ＢＰ算法。对该算法性能的实验研究表明：这种改进算法不但方便了用线性模拟集成运算放大电路实现神经网络，而且具有学习速度快，映射能力强等优点。根据本文算法设计的神经网络分类器，无论是计算机仿真，还是模拟电路实现，都得到了比较高的识别率。     

Learning in the multiple class random neural network

Spiked recurrent neural networks with "multiple classes" of signals have been recently introduced by Gelenbe and Fourneau (1999), as an extension of the recurrent spiked random neural network introduced by Gelenbe (1989). These new networks can represent interconnected neurons, which simultaneously process multiple streams of data such as the color information of images, or networks which simultaneously process streams of data from multiple sensors. This paper introduces a learning algorithm which applies both to recurrent and feedforward multiple signal class random neural networks (MCRNNs). It is based on gradient descent optimization of a cost function. The algorithm exploits the analytical properties of the MCRNN and requires the solution of a system of nC linear and nC nonlinear equations (where C is the number of signal classes and n is the number of neurons) each time the network learns a new input-output pair. Thus, the algorithm is of O([nC]/sup 3/) complexity for the recurrent case, and O([nC]/sup 2/) for a feedforward MCRNN. Finally, we apply this learning algorithm to color texture modeling (learning), based on learning the weights of a recurrent network directly from the color texture image. The same trained recurrent network is then used to generate a synthetic texture that imitates the original. This approach is illustrated with various synthetic and natural textures.